Merge branch 'develop' into develop-runs

# Conflicts:
#	python/learn.py
#	python/unitytrainers/trainer.py

.gitignore

 /unity-environment/[Uu]nity[Pp]ackage[Mm]anager/
 /unity-environment/Assets/AssetStoreTools*
 /unity-environment/Assets/Plugins*
+/unity-environment/Assets/Gizmos*
 
 # Tensorflow Model Info
 /models

README.md

 * For more information, in addition to installation and usage
 instructions, see our [documentation home](docs/Readme.md).
 * If you have
-used a version of ML-Agents prior to v0.3, we strongly recommend 
-our [guide on migrating to v0.3](docs/Migrating-v0.3.md).
+used a version of ML-Agents prior to v0.4, we strongly recommend 
+our [guide on migrating from earlier versions](docs/Migrating.md).
 
 ## References
 

docs/Basic-Guide.md

 
 ## Setting up ML-Agents within Unity
 
-In order to use ML-Agents within Unity, you need to change some Unity settings first. Also [TensorFlowSharp plugin](https://github.com/migueldeicaza/TensorFlowSharp) is needed for you to use pretrained model within Unity. 
+In order to use ML-Agents within Unity, you need to change some Unity settings first. Also [TensorFlowSharp plugin](https://s3.amazonaws.com/unity-ml-agents/0.4/TFSharpPlugin.unitypackage) is needed for you to use pretrained model within Unity, which is based on the [TensorFlowSharp repo](https://github.com/migueldeicaza/TensorFlowSharp). 
 
 1. Launch Unity
 2. On the Projects dialog, choose the **Open** option at the top of the window.
 Where:
 - `<run-identifier>` is a string used to separate the results of different training runs
 - And the `--train` tells learn.py to run a training session (rather than inference)
-5. When the message _"Ready to connect with the Editor"_ is displayed on the screen, you can press the :arrow_forward: button in Unity to start training in the Editor.
+5. When the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen, you can press the :arrow_forward: button in Unity to start training in the Editor.
 
 **Note**: Alternatively, you can use an executable rather than the Editor to perform training. Please refer to [this page](Learning-Environment-Executable.md) for instructions on how to build and use an executable.
 

docs/Getting-Started-with-Balance-Ball.md

 **Vector Observation Space**
 
 Before making a decision, an agent collects its observation about its state 
-in the world. ML-Agents classifies vector observations into two types:
-**Continuous** and **Discrete**. The **Continuous** vector observation space 
-collects observations in a vector of floating point numbers. The **Discrete** 
-vector observation space is an index into a table of states. Most of the example
-environments use a continuous vector observation space. 
+in the world. The vector observation is a vector of floating point numbers
+which contain relevant information for the agent to make decisions. 
 
 The Brain instance used in the 3D Balance Ball example uses the **Continuous** 
 vector observation space with a **State Size** of 8. This means that the 
 python3 learn.py --run-id=<run-identifier> --train 
 ```
 
-When the message _"Ready to connect with the Editor"_ is displayed on the screen, you can press the :arrow_forward: button in Unity to start training in the Editor.
+When the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen, you can press the :arrow_forward: button in Unity to start training in the Editor.
 
 **Note**: If you're using Anaconda, don't forget to activate the ml-agents environment first.
 

docs/Installation-Windows.md

 
 ML-Agents supports Windows 10. While it might be possible to run ML-Agents using other versions of Windows, it has not been tested on other versions. Furthermore, ML-Agents has not been tested on a Windows VM such as Bootcamp or Parallels.
 
-To use ML-Agents, you install Python and the required Python packages as outlined below. This guide also covers how set up GPU-based training (for advanced users). GPU-based training is not required for the v0.3 release of ML-Agents. However, training on a GPU might be required by future versions and features.
+To use ML-Agents, you install Python and the required Python packages as outlined below. This guide also covers how set up GPU-based training (for advanced users). GPU-based training is not required for the v0.4 release of ML-Agents. However, training on a GPU might be required by future versions and features.
 
 ## Step 1: Install Python via Anaconda
 
 </p>
 
 After installation, you must open __Anaconda Navigator__ to finish the setup. From the Windows search bar, type _anaconda navigator_. You can close Anaconda Navigator after it opens.
-If environment variables were not created, or if you see the error "conda is not recognized as internal or external command", in System Variables, "Path" add the following new paths:
+
+If environment variables were not created, you will see error "conda is not recognized as internal or external command" when you type `conda` into the command line. To solve this you will need to set the environment variable correctly. 
+
+Type `environment variables` in the search bar (this can be reached by hitting the Windows key or the bottom left Windows button). You should see an option called __Edit the system environment variables__. 
+
+<p align="center">
+    <img src="images/edit_env_var.png" 
+        alt="edit env variables" 
+        width="250" border="10" />
+</p>
+
+From here, click the __Environment Variables__ button. 
+Double click "Path" under __System variable__ to edit the "Path" variable, click __New__ to add the following new paths. 
+
-    %UserProfile%\Anaconda3\Scripts
-    %UserProfile%\Anaconda3\Scripts\conda.exe
-    %UserProfile%\Anaconda3
-    %UserProfile%\Anaconda3\python.exe
+%UserProfile%\Anaconda3\Scripts
+%UserProfile%\Anaconda3\Scripts\conda.exe
+%UserProfile%\Anaconda3
+%UserProfile%\Anaconda3\python.exe
+
+
 ## Step 2: Setup and Activate a New Conda Environment
 
 You will create a new [Conda environment](https://conda.io/docs/) to be used with ML-Agents. This means that all the packages that you install are localized to just this environment. It will not affect any other installation of Python or other environments. Whenever you want to run ML-Agents, you will need activate this Conda environment.
 
 GPU is not required for ML-Agents and won't speed up the PPO algorithm a lot during training(but something in the future will benefit from GPU). This is a guide for advanced users who want to train using GPUs. Additionally, you will need to check if your GPU is CUDA compatible. Please check Nvidia's page [here](https://developer.nvidia.com/cuda-gpus).
 
-As of ML-Agents v0.3, only CUDA v9.0 and cuDNN v7.0.5 is supported.
+As of ML-Agents v0.4, only CUDA v9.0 and cuDNN v7.0.5 is supported.
 
 ### Install Nvidia CUDA toolkit
 

docs/Learning-Environment-Create-New.md

 In such a basic scene, we don't need the Academy to initialize, reset, or otherwise control any objects in the environment so we have the simplest possible Academy implementation:
 
 ```csharp
+using MLAgents;
+
 public class RollerAcademy : Academy { }
 ```
 
 ```csharp
 using System.Collections.Generic;
 using UnityEngine;
+using MLAgents;
 
 public class RollerAgent : Agent 
 {

docs/Learning-Environment-Design-Agents.md

 
 ## Observations
 
-To make decisions, an agent must observe its environment to determine its current state. A state observation can take the following forms:
+To make decisions, an agent must observe its environment in order to infer the state of the world. A state observation can take the following forms:
-* **Continuous Vector** — a feature vector consisting of an array of numbers. 
-* **Discrete Vector** — an index into a state table (typically only useful for the simplest of environments).
+* **Vector Observation** — a feature vector consisting of an array of floating point numbers. 
-When you use the **Continuous** or **Discrete** vector observation space for an agent, implement the `Agent.CollectObservations()` method to create the feature vector or state index. When you use **Visual Observations**, you only need to identify which Unity Camera objects will provide images and the base Agent class handles the rest. You do not need to implement the `CollectObservations()` method when your agent uses visual observations (unless it also uses vector observations).
+When you use vector observations for an agent, implement the `Agent.CollectObservations()` method to create the feature vector. When you use **Visual Observations**, you only need to identify which Unity Camera objects will provide images and the base Agent class handles the rest. You do not need to implement the `CollectObservations()` method when your agent uses visual observations (unless it also uses vector observations).
-### Continuous Vector Observation Space: Feature Vectors
+### Vector Observation Space: Feature Vectors
 
 For agents using a continuous state space, you create a feature vector to represent the agent's observation at each step of the simulation. The Brain class calls the `CollectObservations()` method of each of its agents. Your implementation of this function must call `AddVectorObs` to add vector observations. 
 
 When you set up an Agent's brain in the Unity Editor, set the following properties to use a continuous vector observation:
 
 **Space Size** — The state size must match the length of your feature vector.
-**Space Type** — Set to **Continuous**.
 **Brain Type** — Set to **External** during training; set to **Internal** to use the trained model.
 
 The observation feature vector is a list of floating point numbers, which means you must convert any other data types to a float or a list of floats. 
  
 ### Multiple Visual Observations
 
-Camera observations use rendered textures from one or more cameras in a scene. The brain vectorizes the textures and feeds them into a neural network. You can use camera observations and either continuous feature vector or discrete state observations at the same time.
+Camera observations use rendered textures from one or more cameras in a scene. The brain vectorizes the textures into a 3D Tensor which can be fed into a convolutional neural network (CNN). For more information on CNNs, see [this guide](http://cs231n.github.io/convolutional-networks/). You can use camera observations along side vector observations.
  
 Agents using camera images can capture state of arbitrary complexity and are useful when the state is difficult to describe numerically. However, they are also typically less efficient and slower to train, and sometimes don't succeed at all.  
 
 
 In addition, make sure that the Agent's Brain expects a visual observation. In the Brain inspector, under **Brain Parameters** > **Visual Observations**, specify the number of Cameras the agent is using for its visual observations. For each visual observation, set the width and height of the image (in pixels) and whether or not the observation is color or grayscale (when `Black And White` is checked).
  
-### Discrete Vector Observation Space: Table Lookup
-
-You can use the discrete vector observation space when an agent only has a limited number of possible states and those states can be enumerated by a single number. For instance, the [Basic example environment](Learning-Environment-Examples.md#basic) in ML-Agents defines an agent with a discrete vector observation space. The states of this agent are the integer steps between two linear goals. In the Basic example, the agent learns to move to the goal that provides the greatest reward.
-
-More generally, the discrete vector observation identifier could be an index into a table of the possible states. However, tables quickly become unwieldy as the environment becomes more complex. For example, even a simple game like [tic-tac-toe has 765 possible states](https://en.wikipedia.org/wiki/Game_complexity) (far more if you don't reduce the number of observations by combining those that are rotations or reflections of each other).
-
-To implement a discrete state observation, implement the `CollectObservations()` method of your Agent subclass and return a `List` containing a single number representing the state:
-
-```csharp
-public override void CollectObservations()
-{
-    AddVectorObs(stateIndex);  // stateIndex is the state identifier
-}
-```
-
 ## Vector Actions
 
 An action is an instruction from the brain that the agent carries out. The action is passed to the agent as a parameter when the Academy invokes the agent's `AgentAction()` function. When you specify that the vector action space is **Continuous**, the action parameter passed to the agent is an array of control signals with length equal to the `Vector Action Space Size` property.  When you specify a **Discrete** vector action space type, the action parameter is an array containing only a single value, which is an index into your list or table of commands. In the **Discrete** vector action space type, the `Vector Action Space Size` is the number of elements in your action table. Set the `Vector Action Space Size` and `Vector Action Space Type` properties on the Brain object assigned to the agent (using the Unity Editor Inspector window). 

docs/Learning-Environment-Design-Brains.md

 
 * `Brain Parameters` - Define vector observations, visual observation, and vector actions for the Brain.
     * `Vector Observation` 
-    	* `Space Type` - Corresponds to whether the observation vector contains a single integer (Discrete) or a series of real-valued floats (Continuous).
-    	* `Space Size` - Length of vector observation for brain (In _Continuous_ space type). Or number of possible values (in _Discrete_ space type).
+    	* `Space Size` - Length of vector observation for brain.
 		* `Stacked Vectors` - The number of previous vector observations that will be stacked and used collectively for decision making. This results in the effective size of the vector observation being passed to the brain being: _Space Size_ x _Stacked Vectors_.
 	* `Visual Observations`	- Describes height, width, and whether to grayscale visual observations for the Brain.
 	* `Vector Action`

docs/Learning-Environment-Design-Heuristic-Brains.md

 
 ```csharp
 using UnityEngine;
+using MLAgents;
 
 public class HeuristicLogic : MonoBehaviour, Decision 
 {

docs/Learning-Environment-Examples.md

     * +0.1 for arriving at suboptimal state.
     * +1.0 for arriving at optimal state.
 * Brains: One brain with the following observation/action space.
-    * Vector Observation space: (Discrete) One variable corresponding to current state.
+    * Vector Observation space: One variable corresponding to current state.
     * Vector Action space: (Discrete) Two possible actions (Move left, move right).
     * Visual Observations: None.
 * Reset Parameters: None
     * +0.1 for every step the ball remains on the platform. 
     * -1.0 if the ball falls from the platform.
 * Brains: One brain with the following observation/action space.
-    * Vector Observation space: (Continuous) 8 variables corresponding to rotation of platform, and position, rotation, and velocity of ball.
-    * Vector Observation space (Hard Version): (Continuous) 5 variables corresponding to rotation of platform and position and rotation of ball.
+    * Vector Observation space: 8 variables corresponding to rotation of platform, and position, rotation, and velocity of ball.
+    * Vector Observation space (Hard Version): 5 variables corresponding to rotation of platform and position and rotation of ball.
     * Vector Action space: (Continuous) Size of 2, with one value corresponding to X-rotation, and the other to Z-rotation.
     * Visual Observations: None.
 * Reset Parameters: None
     * +0.1 To agent when hitting ball over net.
     * -0.1 To agent who let ball hit their ground, or hit ball out of bounds.
 * Brains: One brain with the following observation/action space.
-    * Vector Observation space: (Continuous) 8 variables corresponding to position and velocity of ball and racket.
+    * Vector Observation space: 8 variables corresponding to position and velocity of ball and racket.
     * Vector Action space: (Continuous) Size of 2, corresponding to movement toward net or away from net, and jumping.
     * Visual Observations: None.
 * Reset Parameters: One, corresponding to size of ball.
     * -0.0025 for every step.
     * +1.0 if the block touches the goal.
 * Brains: One brain with the following observation/action space.
-    * Vector Observation space: (Continuous) 15 variables corresponding to position and velocities of agent, block, and goal.
+    * Vector Observation space: 15 variables corresponding to position and velocities of agent, block, and goal.
     * Vector Action space: (Continuous) Size of 2, corresponding to movement in X and Z directions.
     * Visual Observations (Optional): One first-person camera. Use `VisualPushBlock` scene.
 * Reset Parameters: None.
     * +1.0 if the agent touches the goal.
     * -1.0 if the agent falls off the platform.
 * Brains: Two brains, each with the following observation/action space.
-    * Vector Observation space: (Continuous) 16 variables corresponding to position and velocities of agent, block, and goal, plus the height of the wall.
+    * Vector Observation space: 16 variables corresponding to position and velocities of agent, block, and goal, plus the height of the wall.
     * Vector Action space: (Discrete) Size of 74, corresponding to 14 raycasts each detecting 4 possible objects. plus the global position of the agent and whether or not the agent is grounded.
     * Visual Observations: None.
 * Reset Parameters: 4, corresponding to the height of the possible walls.
 * Agent Reward Function (independent): 
     * +0.1 Each step agent's hand is in goal location.
 * Brains: One brain with the following observation/action space.
-    * Vector Observation space: (Continuous) 26 variables corresponding to position, rotation, velocity, and angular velocities of the two arm Rigidbodies.
+    * Vector Observation space: 26 variables corresponding to position, rotation, velocity, and angular velocities of the two arm Rigidbodies.
     * Vector Action space: (Continuous) Size of 4, corresponding to torque applicable to two joints. 
     * Visual Observations: None.
 * Reset Parameters: Two, corresponding to goal size, and goal movement speed.
 ![Crawler](images/crawler.png)
 
 * Set-up: A creature with 4 arms and 4 forearms.
-* Goal: The agents must move its body along the x axis without falling.
+* Goal: The agents must move its body toward the goal direction without falling.
+    * `CrawlerStaticTarget` - Goal direction is always forward.
+    * `CrawlerDynamicTarget`- Goal direction is randomized.
-    * +1 times velocity in the x direction
-    * -1 for falling.
-    * -0.01 times the action squared
-    * -0.05 times y position change
-    * -0.05 times velocity in the z direction 
+    * +0.03 times body velocity in the goal direction.
+    * +0.01 times body direction alignment with goal direction.
-    * Vector Observation space: (Continuous) 117 variables corresponding to position, rotation, velocity, and angular velocities of each limb plus the acceleration and angular acceleration of the body.
-    * Vector Action space: (Continuous) Size of 12, corresponding to torque applicable to 12 joints. 
+    * Vector Observation space: 117 variables corresponding to position, rotation, velocity, and angular velocities of each limb plus the acceleration and angular acceleration of the body.
+    * Vector Action space: (Continuous) Size of 20, corresponding to target rotations for joints. 
     * Visual Observations: None.
 * Reset Parameters: None
 * Benchmark Mean Reward: 2000
     * +1 for interaction with yellow banana
     * -1 for interaction with blue banana.
 * Brains: One brain with the following observation/action space.
-    * Vector Observation space: (Continuous) 53 corresponding to velocity of agent (2), whether agent is frozen and/or shot its laser (2), plus ray-based perception of objects around agent's forward direction (49; 7 raycast angles with 7 measurements for each).
+    * Vector Observation space: 53 corresponding to velocity of agent (2), whether agent is frozen and/or shot its laser (2), plus ray-based perception of objects around agent's forward direction (49; 7 raycast angles with 7 measurements for each).
     * Vector Action space: (Continuous) Size of 3, corresponding to forward movement, y-axis rotation, and whether to use laser to disable other agents.
     * Visual Observations (Optional): First-person camera per-agent. Use `VisualBanana` scene. 
 * Reset Parameters: None.
     * -0.1 For moving to incorrect goal.
     * -0.0003 Existential penalty.
 * Brains: One brain with the following observation/action space:
-    * Vector Observation space: (Continuous) 30 corresponding to local ray-casts detecting objects, goals, and walls.
+    * Vector Observation space: 30 corresponding to local ray-casts detecting objects, goals, and walls.
     * Vector Action space: (Discrete) 4 corresponding to agent rotation and forward/backward movement.
     * Visual Observations (Optional): First-person view for the agent. Use `VisualHallway` scene.
 * Reset Parameters: None.
     * -1 For bouncing out of bounds.
     * -0.05 Times the action squared. Energy expenditure penalty.
 * Brains: One brain with the following observation/action space:
-    * Vector Observation space: (Continuous) 6 corresponding to local position of agent and banana.
+    * Vector Observation space: 6 corresponding to local position of agent and banana.
     * Vector Action space: (Continuous) 3 corresponding to agent force applied for the jump.
     * Visual Observations: None.
 * Reset Parameters: None.
         * +0.1 When ball enters opponents goal.
         * +0.001 Existential bonus.
 * Brains: Two brain with the following observation/action space:
-    * Vector Observation space: (Continuous) 112 corresponding to local 14 ray casts, each detecting 7 possible object types, along with the object's distance. Perception is in 180 degree view from front of agent.
+    * Vector Observation space: 112 corresponding to local 14 ray casts, each detecting 7 possible object types, along with the object's distance. Perception is in 180 degree view from front of agent.
     * Vector Action space: (Discrete) 
         * Striker: 6 corresponding to forward, backward, sideways movement, as well as rotation.
         * Goalie: 4 corresponding to forward, backward, sideways movement.
     * +0.01 times body direction alignment with goal direction.
     * -0.01 times head velocity difference from body velocity.
 * Brains: One brain with the following observation/action space.
-    * Vector Observation space: (Continuous) 215 variables corresponding to position, rotation, velocity, and angular velocities of each limb, along with goal direction.
+    * Vector Observation space: 215 variables corresponding to position, rotation, velocity, and angular velocities of each limb, along with goal direction.
     * Vector Action space: (Continuous) Size of 39, corresponding to target rotations applicable to the joints. 
     * Visual Observations: None.
 * Reset Parameters: None.
 * Agent Reward Function (independent):
     * +2 For moving to golden brick (minus 0.001 per step).
 * Brains: One brain with the following observation/action space:
-    * Vector Observation space: (Continuous) 148 corresponding to local ray-casts detecting switch, bricks, golden brick, and walls, plus variable indicating switch state.
+    * Vector Observation space: 148 corresponding to local ray-casts detecting switch, bricks, golden brick, and walls, plus variable indicating switch state.
     * Vector Action space: (Discrete) 4 corresponding to agent rotation and forward/backward movement.
     * Visual Observations (Optional): First-person camera per-agent. Use `VisualPyramids` scene. 
 * Reset Parameters: None.

docs/ML-Agents-Overview.md

 Observations can be numeric and/or visual. Numeric observations measure 
 attributes of the environment from the point of view of the agent. For
 our medic this would be attributes of the battlefield that are visible to it.
-Observations can either be _discrete_ or _continuous_ depending on the complexity
-of the game and agent. For most interesting environments, an agent will require 
-several continuous numeric observations, while for simple environments with 
-a small number of unique configurations, a discrete observation will suffice. 
+For most interesting environments, an agent will require 
+several continuous numeric observations. 
 Visual observations, on the other hand, are images generated from the cameras 
 attached to the agent and represent what the agent is seeing at that point
 in time. It is common to confuse an agent's observation with the environment

docs/Python-API.md

 * `worker_id` indicates which port to use for communication with the environment. For use in parallel training regimes such as A3C.
 * `seed` indicates the seed to use when generating random numbers during the training process. In environments which do not involve physics calculations, setting the seed enables reproducible experimentation by ensuring that the environment and trainers utilize the same random seed.
 
-If you want to directly interact with the Editor, you need to use `file_name=None`, then press the :arrow_forward: button in the Editor when the message _"Ready to connect with the Editor"_ is displayed on the screen
+If you want to directly interact with the Editor, you need to use `file_name=None`, then press the :arrow_forward: button in the Editor when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen
 
 ## Interacting with a Unity Environment
 
-* **`vector_observations`** : A two dimensional numpy array of dimension `(batch size, vector observation size)` if the vector observation space is continuous and `(batch size, 1)` if the vector observation space is discrete.
+* **`vector_observations`** : A two dimensional numpy array of dimension `(batch size, vector observation size)`.
 * **`text_observations`** : A list of string corresponding to the agents text observations.
 * **`memories`** : A two dimensional numpy array of dimension `(batch size, memory size)` which corresponds to the memories sent at the previous step.
 * **`rewards`** : A list as long as the number of agents using the brain containing the rewards they each obtained at the previous step. 

docs/Readme.md

  * [Using TensorBoard to Observe Training](Using-Tensorboard.md)
 
 ## Help
- * [Migrating to ML-Agents v0.3](Migrating-v0.3.md)
+ * [Migrating from earlier versions of ML-Agents](Migrating.md)
  * [Frequently Asked Questions](FAQ.md)
  * [ML-Agents Glossary](Glossary.md)
  * [Limitations](Limitations.md)

docs/Training-Imitation-Learning.md

 3. Set the "Student" brain to External mode.
 4. Link the brains to the desired agents (one agent as the teacher and at least one agent as a student).
 5. In `trainer_config.yaml`, add an entry for the "Student" brain. Set the `trainer` parameter of this entry to `imitation`, and the `brain_to_imitate` parameter to the name of the teacher brain: "Teacher". Additionally, set `batches_per_epoch`, which controls how much training to do each moment. Increase the `max_steps` option if you'd like to keep training the agents for a longer period of time.
-6. Launch the training process with `python3 python/learn.py --train --slow`, and press the :arrow_forward: button in Unity when the message _"Ready to connect with the Editor"_ is displayed on the screen
+6. Launch the training process with `python3 python/learn.py --train --slow`, and press the :arrow_forward: button in Unity when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen
 7. From the Unity window, control the agent with the Teacher brain by providing "teacher demonstrations" of the behavior you would like to see.
 8. Watch as the agent(s) with the student brain attached begin to behave similarly to the demonstrations.
 9. Once the Student agents are exhibiting the desired behavior, end the training process with `CTL+C` from the command line.

docs/Training-ML-Agents.md

     python3 learn.py <env_name> --run-id=<run-identifier> --train
 
 where 
- * `<env_name>`__(Optional)__ is the name (including path) of your Unity executable containing the agents to be trained. If `<env_name>` is not passed, the training will happen in the Editor. Press the :arrow_forward: button in Unity when the message _"Ready to connect with the Editor"_ is displayed on the screen.
+ * `<env_name>`__(Optional)__ is the name (including path) of your Unity executable containing the agents to be trained. If `<env_name>` is not passed, the training will happen in the Editor. Press the :arrow_forward: button in Unity when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen.
  * `<run-identifier>` is an optional identifier you can use to identify the results of individual training runs.
 
 For example, suppose you have a project in Unity named "CatsOnBicycles" which contains agents ready to train. To perform the training:
 * `--train` – Specifies whether to train model or only run in inference mode. When training, **always** use the `--train` option.
 * `--worker-id=<n>` – When you are running more than one training environment at the same time, assign each a unique worker-id number. The worker-id is added to the communication port opened between the current instance of learn.py and the ExternalCommunicator object in the Unity environment. Defaults to 0.
 * `--docker-target-name=<dt>` – The Docker Volume on which to store curriculum, executable and model files. See [Using Docker](Using-Docker.md).
+* `--no-graphics` - Specify this option to run the Unity executable in `-batchmode` and doesn't initialize the graphics driver. Use this only if your training doesn't involve visual observations (reading from Pixels). See [here](https://docs.unity3d.com/Manual/CommandLineArguments.html) for more details.
 
 ### Training config file
 

docs/Training-on-Amazon-Web-Service.md

 # Training on Amazon Web Service
 
-This page contains instructions for setting up an EC2 instance on Amazon Web Service for training ML-Agents environments. Visual observations is not supported currently and you need to enable headless mode.
+This page contains instructions for setting up an EC2 instance on Amazon Web Service for training ML-Agents environments. 
-## Recommended AMI
-You can get started with an EC2 instance with the Deep Learning AMI (Ubuntu) listed under AWS Marketplace [AMI](https://aws.amazon.com/marketplace/pp/B077GCH38C). Choose the python3 environment within that ami which gives you the python3 and CUDA 9.0 environment.
+## Preconfigured AMI
+
+We've prepared an preconfigured AMI for you with the ID: `ami-6880c317` in the `us-east-1` region. It was created as a modification of [Deep Learning AMI (Ubuntu)](https://aws.amazon.com/marketplace/pp/B077GCH38C). If you want to do training without the headless mode, you need to enable X Server on it. After launching your EC2 instance using the ami and ssh into it, run the following commands to enable it:
+
+```
+//Start the X Server, press Enter to come to the command line
+sudo /usr/bin/X :0 &
+
+//Check if Xorg process is running
+//You will have a list of processes running on the GPU, Xorg should be in the list, as shown below
+nvidia-smi
+/*
+ * Thu Jun 14 20:27:26 2018
+ * +-----------------------------------------------------------------------------+
+ * | NVIDIA-SMI 390.67                 Driver Version: 390.67                    |
+ * |-------------------------------+----------------------+----------------------+
+ * | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+ * | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+ * |===============================+======================+======================|
+ * |   0  Tesla K80           On   | 00000000:00:1E.0 Off |                    0 |
+ * | N/A   35C    P8    31W / 149W |      9MiB / 11441MiB |      0%      Default |
+ * +-------------------------------+----------------------+----------------------+
+ *
+ * +-----------------------------------------------------------------------------+
+ * | Processes:                                                       GPU Memory |
+ * |  GPU       PID   Type   Process name                             Usage      |
+ * |=============================================================================|
+ * |    0      2331      G   /usr/lib/xorg/Xorg                             8MiB |
+ * +-----------------------------------------------------------------------------+
+ */
+
+//Make the ubuntu use X Server for display
+export DISPLAY=:0
+```
-To begin with, you will need an EC2 instance which contains the latest Nvidia drivers, CUDA9, and cuDNN.  There are a number of external tutorials which describe this (Note: You will need to tweak some steps in these tutorials for CUDA 9):
+You could also choose to configure your own instance. To begin with, you will need an EC2 instance which contains the latest Nvidia drivers, CUDA9, and cuDNN. In this tutorial we used the [Deep Learning AMI (Ubuntu)](https://aws.amazon.com/marketplace/pp/B077GCH38C) listed under AWS Marketplace with a p2.xlarge instance. 
-* [Getting CUDA 8 to Work With openAI Gym on AWS and Compiling TensorFlow for CUDA 8 Compatibility](https://davidsanwald.github.io/2016/11/13/building-tensorflow-with-gpu-support.html)    
-* [Installing TensorFlow on an AWS EC2 P2 GPU Instance](http://expressionflow.com/2016/10/09/installing-tensorflow-on-an-aws-ec2-p2-gpu-instance/)   
-* [Updating Nvidia CUDA to 8.0.x in Ubuntu 16.04 – EC2 Gx instance](https://aichamp.wordpress.com/2016/11/09/updating-nvidia-cuda-to-8-0-x-in-ubuntu-16-04-ec2-gx-instance/)
+### Installing ML-Agents on the instance
-## Installing ML-Agents
+After launching your EC2 instance using the ami and ssh into it:
-1. Move `python` sub-folder of this ml-agents repo to the remote ECS instance, and set it as the working directory.
-2. Install the required packages with `pip3 install .`.
+1. Activate the python3 environment
-## Testing
+    ```
+    source activate python3
+    ```
-To verify that all steps worked correctly:
+2. Clone the ML-Agents repo and install the required python packages
+
+    ```
+    git clone https://github.com/Unity-Technologies/ml-agents.git
+    cd ml-agents/python
+    pip3 install .
+    ```
+
+### Setting up X Server (optional)
+
+X Server setup is only necessary if you want to do training that requires visual observation input. _Instructions here are adapted from this [Medium post](https://medium.com/towards-data-science/how-to-run-unity-on-amazon-cloud-or-without-monitor-3c10ce022639) on running general Unity applications in the cloud._
+
+Current limitations of the Unity Engine require that a screen be available to render to when using visual observations. In order to make this possible when training on a remote server, a virtual screen is required. We can do this by installing Xorg and creating a virtual screen. Once installed and created, we can display the Unity environment in the virtual environment, and train as we would on a local machine. Ensure that `headless` mode is disabled when building linux executables which use visual observations.
+
+1. Install and setup Xorg:
+
+    ```
+    //Install Xorg
+    sudo apt-get update
+    sudo apt-get install -y xserver-xorg mesa-utils
+    sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024
+
+    //Get the BusID information
+    nvidia-xconfig --query-gpu-info
+
+    //Add the BusID information to your /etc/X11/xorg.conf file
+    sudo sed -i 's/    BoardName      "Tesla K80"/    BoardName      "Tesla K80"\n    BusID          "0:30:0"/g' /etc/X11/xorg.conf
+
+    //Remove the Section "Files" from the /etc/X11/xorg.conf file
+    sudo vim /etc/X11/xorg.conf //And remove two lines that contain Section "Files" and EndSection
+    ```
+
+2. Update and setup Nvidia driver:
+
+    ```
+    //Download and install the latest Nvidia driver for ubuntu
+    wget http://download.nvidia.com/XFree86/Linux-x86_64/390.67/NVIDIA-Linux-x86_64-390.67.run
+    sudo /bin/bash ./NVIDIA-Linux-x86_64-390.67.run --accept-license --no-questions --ui=none
+
+    //Disable Nouveau as it will clash with the Nvidia driver
+    sudo echo 'blacklist nouveau'  | sudo tee -a /etc/modprobe.d/blacklist.conf
+    sudo echo 'options nouveau modeset=0'  | sudo tee -a /etc/modprobe.d/blacklist.conf
+    sudo echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf
+    sudo update-initramfs -u
+    ```
+
+2. Restart the EC2 instance:
+
+    ```
+    sudo reboot now
+    ```
+
+3. Make sure there are no Xorg processes running:
+
+   ```
+   //Kill any possible running Xorg processes
+   //Note that you might have to run this command multiple times depending on how Xorg is configured.
+   sudo killall Xorg
+
+   //Check if there is any Xorg process left
+   //You will have a list of processes running on the GPU, Xorg should not be in the list, as shown below.
+   nvidia-smi
+   /*
+    * Thu Jun 14 20:21:11 2018
+    * +-----------------------------------------------------------------------------+
+    * | NVIDIA-SMI 390.67                 Driver Version: 390.67                    |
+    * |-------------------------------+----------------------+----------------------+
+    * | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+    * | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+    * |===============================+======================+======================|
+    * |   0  Tesla K80           On   | 00000000:00:1E.0 Off |                    0 |
+    * | N/A   37C    P8    31W / 149W |      0MiB / 11441MiB |      0%      Default |
+    * +-------------------------------+----------------------+----------------------+
+    *
+    * +-----------------------------------------------------------------------------+
+    * | Processes:                                                       GPU Memory |
+    * |  GPU       PID   Type   Process name                             Usage      |
+    * |=============================================================================|
+    * |  No running processes found                                                 |
+    * +-----------------------------------------------------------------------------+
+    */
+   ```
+
+4. Start X Server and make the ubuntu use X Server for display:
+
+    ```
+    //Start the X Server, press Enter to come to the command line
+    sudo /usr/bin/X :0 &
+
+    //Check if Xorg process is running
+    //You will have a list of processes running on the GPU, Xorg should be in the list.
+    nvidia-smi
+
+    //Make the ubuntu use X Server for display
+    export DISPLAY=:0
+    ```
+
+5. Ensure the Xorg is correctly configured:
+
+    ```
+    //For more information on glxgears, see ftp://www.x.org/pub/X11R6.8.1/doc/glxgears.1.html. 
+    glxgears
+    //If Xorg is configured correctly, you should see the following message
+    /*
+     * Running synchronized to the vertical refresh.  The framerate should be
+     * approximately the same as the monitor refresh rate.
+     * 137296 frames in 5.0 seconds = 27459.053 FPS
+     * 141674 frames in 5.0 seconds = 28334.779 FPS
+     * 141490 frames in 5.0 seconds = 28297.875 FPS
+     */
+    ```
+
+## Training on EC2 instance
-4. Check Headless Mode. 
+4. Check Headless Mode (If you haven't setup the X Server).
-6. Upload the executable to your EC2 instance.
+6. Upload the executable to your EC2 instance within `ml-agents/python` folder.
-```python
-from unityagents import UnityEnvironment
+    ```python
+    from unityagents import UnityEnvironment
-env = UnityEnvironment(<your_env>)
-```
-Where `<your_env>` corresponds to the path to your environment executable.
- 
-You should receive a message confirming that the environment was loaded successfully.
+    env = UnityEnvironment(<your_env>)
+    ```
+    Where `<your_env>` corresponds to the path to your environment executable.
+
+    You should receive a message confirming that the environment was loaded successfully.
+8. Train the executable
+
+    ```
+    //cd into your ml-agents/python folder
+    chmod +x <your_env>.x86_64
+    python learn.py <your_env> --train
+    ```

docs/Using-Docker.md

 Notes on argument values:
 - `<container-name>` is used to identify the container (in case you want to interrupt and terminate it). This is optional and Docker will generate a random name if this is not set. _Note that this must be unique for every run of a Docker image._
 - `<image-name>` references the image name used when building the container.
-- `<environemnt-name>` __(Optional)__: If you are training with a linux executable, this is the name of the executable. If you are training in the Editor, do not pass a `<environemnt-name>` argument and press the :arrow_forward: button in Unity when the message _"Ready to connect with the Editor"_ is displayed on the screen.
+- `<environemnt-name>` __(Optional)__: If you are training with a linux executable, this is the name of the executable. If you are training in the Editor, do not pass a `<environemnt-name>` argument and press the :arrow_forward: button in Unity when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen.
 - `source`: Reference to the path in your host OS where you will store the Unity executable. 
 - `target`: Tells Docker to mount the `source` path as a disk with this name. 
 - `docker-target-name`: Tells the ML-Agents Python package what the name of the disk where it can read the Unity executable and store the graph. **This should therefore be identical to `target`.**

docs/dox-ml-agents.conf

 # could be handy for archiving the generated documentation or if some version
 # control system is used.
 
-PROJECT_NUMBER         = v0.3
+PROJECT_NUMBER         = v0.4
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
 # for a project that appears at the top of each page and should give viewer a

docs/localized/zh-CN/README.md

 **除了安装和使用说明外，如需更多信息，
 请参阅我们的[文档主页](docs/Readme.md)。**如果您使用的
 是 v0.3 之前的 ML-Agents 版本，强烈建议您参考
-我们的[关于迁移到 v0.3 的指南](/docs/Migrating-v0.3.md)。
+我们的[关于迁移到 v0.3 的指南](/docs/Migrating.md)。
 
 我们还发布了一系列与 ML-Agents 相关的博客文章：
 - reinforcement learning（强化学习）概念概述

docs/localized/zh-CN/docs/Readme.md

  * [如何使用 TensorBoard 观察训练过程](/docs/Using-Tensorboard.md)
 
 ## 帮助
- * [如何从老版本升级到 ML-Agents v0.3](/docs/Migrating-v0.3.md)
+ * [如何从老版本升级到 ML-Agents v0.3](/docs/Migrating.md)
  * [常见问题](/docs/FAQ.md)
  * [ML-Agents 术语表](/docs/Glossary.md)
  * [ML-Agents 尚未实现功能](/docs/Limitations.md)

python/communicator_objects/agent_action_proto_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/agent_action_proto.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n-communicator_objects/agent_action_proto.proto\x12\x14\x63ommunicator_objects\"R\n\x10\x41gentActionProto\x12\x16\n\x0evector_actions\x18\x01 \x03(\x02\x12\x14\n\x0ctext_actions\x18\x02 \x01(\t\x12\x10\n\x08memories\x18\x03 \x03(\x02\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
       has_default_value=False, default_value=[],
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='text_actions', full_name='communicator_objects.AgentActionProto.text_actions', index=1,
       number=2, type=9, cpp_type=9, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='memories', full_name='communicator_objects.AgentActionProto.memories', index=2,
       number=3, type=2, cpp_type=6, label=3,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(AgentActionProto)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/agent_info_proto_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/agent_info_proto.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n+communicator_objects/agent_info_proto.proto\x12\x14\x63ommunicator_objects\"\xfd\x01\n\x0e\x41gentInfoProto\x12\"\n\x1astacked_vector_observation\x18\x01 \x03(\x02\x12\x1b\n\x13visual_observations\x18\x02 \x03(\x0c\x12\x18\n\x10text_observation\x18\x03 \x01(\t\x12\x1d\n\x15stored_vector_actions\x18\x04 \x03(\x02\x12\x1b\n\x13stored_text_actions\x18\x05 \x01(\t\x12\x10\n\x08memories\x18\x06 \x03(\x02\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
       has_default_value=False, default_value=[],
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='visual_observations', full_name='communicator_objects.AgentInfoProto.visual_observations', index=1,
       number=2, type=12, cpp_type=9, label=3,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='text_observation', full_name='communicator_objects.AgentInfoProto.text_observation', index=2,
       number=3, type=9, cpp_type=9, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='stored_vector_actions', full_name='communicator_objects.AgentInfoProto.stored_vector_actions', index=3,
       number=4, type=2, cpp_type=6, label=3,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='stored_text_actions', full_name='communicator_objects.AgentInfoProto.stored_text_actions', index=4,
       number=5, type=9, cpp_type=9, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='memories', full_name='communicator_objects.AgentInfoProto.memories', index=5,
       number=6, type=2, cpp_type=6, label=3,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='reward', full_name='communicator_objects.AgentInfoProto.reward', index=6,
       number=7, type=2, cpp_type=6, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='done', full_name='communicator_objects.AgentInfoProto.done', index=7,
       number=8, type=8, cpp_type=7, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='max_step_reached', full_name='communicator_objects.AgentInfoProto.max_step_reached', index=8,
       number=9, type=8, cpp_type=7, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='id', full_name='communicator_objects.AgentInfoProto.id', index=9,
       number=10, type=5, cpp_type=1, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(AgentInfoProto)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/brain_parameters_proto_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/brain_parameters_proto.proto',
   package='communicator_objects',
   syntax='proto3',
-  serialized_pb=_b('\n1communicator_objects/brain_parameters_proto.proto\x12\x14\x63ommunicator_objects\x1a+communicator_objects/resolution_proto.proto\x1a+communicator_objects/brain_type_proto.proto\x1a+communicator_objects/space_type_proto.proto\"\xc6\x03\n\x14\x42rainParametersProto\x12\x1f\n\x17vector_observation_size\x18\x01 \x01(\x05\x12\'\n\x1fnum_stacked_vector_observations\x18\x02 \x01(\x05\x12\x1a\n\x12vector_action_size\x18\x03 \x01(\x05\x12\x41\n\x12\x63\x61mera_resolutions\x18\x04 \x03(\x0b\x32%.communicator_objects.ResolutionProto\x12\"\n\x1avector_action_descriptions\x18\x05 \x03(\t\x12\x46\n\x18vector_action_space_type\x18\x06 \x01(\x0e\x32$.communicator_objects.SpaceTypeProto\x12K\n\x1dvector_observation_space_type\x18\x07 \x01(\x0e\x32$.communicator_objects.SpaceTypeProto\x12\x12\n\nbrain_name\x18\x08 \x01(\t\x12\x38\n\nbrain_type\x18\t \x01(\x0e\x32$.communicator_objects.BrainTypeProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
+  serialized_pb=_b('\n1communicator_objects/brain_parameters_proto.proto\x12\x14\x63ommunicator_objects\x1a+communicator_objects/resolution_proto.proto\x1a+communicator_objects/brain_type_proto.proto\x1a+communicator_objects/space_type_proto.proto\"\xf9\x02\n\x14\x42rainParametersProto\x12\x1f\n\x17vector_observation_size\x18\x01 \x01(\x05\x12\'\n\x1fnum_stacked_vector_observations\x18\x02 \x01(\x05\x12\x1a\n\x12vector_action_size\x18\x03 \x01(\x05\x12\x41\n\x12\x63\x61mera_resolutions\x18\x04 \x03(\x0b\x32%.communicator_objects.ResolutionProto\x12\"\n\x1avector_action_descriptions\x18\x05 \x03(\t\x12\x46\n\x18vector_action_space_type\x18\x06 \x01(\x0e\x32$.communicator_objects.SpaceTypeProto\x12\x12\n\nbrain_name\x18\x07 \x01(\t\x12\x38\n\nbrain_type\x18\x08 \x01(\x0e\x32$.communicator_objects.BrainTypeProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[communicator__objects_dot_resolution__proto__pb2.DESCRIPTOR,communicator__objects_dot_brain__type__proto__pb2.DESCRIPTOR,communicator__objects_dot_space__type__proto__pb2.DESCRIPTOR,])
 
       has_default_value=False, default_value=0,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='num_stacked_vector_observations', full_name='communicator_objects.BrainParametersProto.num_stacked_vector_observations', index=1,
       number=2, type=5, cpp_type=1, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='vector_action_size', full_name='communicator_objects.BrainParametersProto.vector_action_size', index=2,
       number=3, type=5, cpp_type=1, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='camera_resolutions', full_name='communicator_objects.BrainParametersProto.camera_resolutions', index=3,
       number=4, type=11, cpp_type=10, label=3,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='vector_action_descriptions', full_name='communicator_objects.BrainParametersProto.vector_action_descriptions', index=4,
       number=5, type=9, cpp_type=9, label=3,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='vector_action_space_type', full_name='communicator_objects.BrainParametersProto.vector_action_space_type', index=5,
       number=6, type=14, cpp_type=8, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
-      name='vector_observation_space_type', full_name='communicator_objects.BrainParametersProto.vector_observation_space_type', index=6,
-      number=7, type=14, cpp_type=8, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='brain_name', full_name='communicator_objects.BrainParametersProto.brain_name', index=7,
-      number=8, type=9, cpp_type=9, label=1,
+      name='brain_name', full_name='communicator_objects.BrainParametersProto.brain_name', index=6,
+      number=7, type=9, cpp_type=9, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
-      name='brain_type', full_name='communicator_objects.BrainParametersProto.brain_type', index=8,
-      number=9, type=14, cpp_type=8, label=1,
+      name='brain_type', full_name='communicator_objects.BrainParametersProto.brain_type', index=7,
+      number=8, type=14, cpp_type=8, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
-  serialized_end=665,
+  serialized_end=588,
-_BRAINPARAMETERSPROTO.fields_by_name['vector_observation_space_type'].enum_type = communicator__objects_dot_space__type__proto__pb2._SPACETYPEPROTO
 _BRAINPARAMETERSPROTO.fields_by_name['brain_type'].enum_type = communicator__objects_dot_brain__type__proto__pb2._BRAINTYPEPROTO
 DESCRIPTOR.message_types_by_name['BrainParametersProto'] = _BRAINPARAMETERSPROTO
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)
 _sym_db.RegisterMessage(BrainParametersProto)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/brain_type_proto_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/brain_type_proto.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n+communicator_objects/brain_type_proto.proto\x12\x14\x63ommunicator_objects\x1a+communicator_objects/resolution_proto.proto*G\n\x0e\x42rainTypeProto\x12\n\n\x06Player\x10\x00\x12\r\n\tHeuristic\x10\x01\x12\x0c\n\x08\x45xternal\x10\x02\x12\x0c\n\x08Internal\x10\x03\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[communicator__objects_dot_resolution__proto__pb2.DESCRIPTOR,])
   values=[
     _descriptor.EnumValueDescriptor(
       name='Player', index=0, number=0,
-      options=None,
+      serialized_options=None,
-      options=None,
+      serialized_options=None,
-      options=None,
+      serialized_options=None,
-      options=None,
+      serialized_options=None,
-  options=None,
+  serialized_options=None,
   serialized_start=114,
   serialized_end=185,
 )
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/command_proto_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/command_proto.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n(communicator_objects/command_proto.proto\x12\x14\x63ommunicator_objects*-\n\x0c\x43ommandProto\x12\x08\n\x04STEP\x10\x00\x12\t\n\x05RESET\x10\x01\x12\x08\n\x04QUIT\x10\x02\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
   values=[
     _descriptor.EnumValueDescriptor(
       name='STEP', index=0, number=0,
-      options=None,
+      serialized_options=None,
-      options=None,
+      serialized_options=None,
-      options=None,
+      serialized_options=None,
-  options=None,
+  serialized_options=None,
   serialized_start=66,
   serialized_end=111,
 )
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/engine_configuration_proto_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/engine_configuration_proto.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n5communicator_objects/engine_configuration_proto.proto\x12\x14\x63ommunicator_objects\"\x95\x01\n\x18\x45ngineConfigurationProto\x12\r\n\x05width\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\x15\n\rquality_level\x18\x03 \x01(\x05\x12\x12\n\ntime_scale\x18\x04 \x01(\x02\x12\x19\n\x11target_frame_rate\x18\x05 \x01(\x05\x12\x14\n\x0cshow_monitor\x18\x06 \x01(\x08\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
       has_default_value=False, default_value=0,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='height', full_name='communicator_objects.EngineConfigurationProto.height', index=1,
       number=2, type=5, cpp_type=1, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='quality_level', full_name='communicator_objects.EngineConfigurationProto.quality_level', index=2,
       number=3, type=5, cpp_type=1, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='time_scale', full_name='communicator_objects.EngineConfigurationProto.time_scale', index=3,
       number=4, type=2, cpp_type=6, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='target_frame_rate', full_name='communicator_objects.EngineConfigurationProto.target_frame_rate', index=4,
       number=5, type=5, cpp_type=1, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='show_monitor', full_name='communicator_objects.EngineConfigurationProto.show_monitor', index=5,
       number=6, type=8, cpp_type=7, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(EngineConfigurationProto)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/environment_parameters_proto_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/environment_parameters_proto.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n7communicator_objects/environment_parameters_proto.proto\x12\x14\x63ommunicator_objects\"\xb5\x01\n\x1a\x45nvironmentParametersProto\x12_\n\x10\x66loat_parameters\x18\x01 \x03(\x0b\x32\x45.communicator_objects.EnvironmentParametersProto.FloatParametersEntry\x1a\x36\n\x14\x46loatParametersEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x02:\x02\x38\x01\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
       has_default_value=False, default_value=_b("").decode('utf-8'),
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='value', full_name='communicator_objects.EnvironmentParametersProto.FloatParametersEntry.value', index=1,
       number=2, type=2, cpp_type=6, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
+  serialized_options=_b('8\001'),
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
       has_default_value=False, default_value=[],
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(EnvironmentParametersProto.FloatParametersEntry)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
-_ENVIRONMENTPARAMETERSPROTO_FLOATPARAMETERSENTRY.has_options = True
-_ENVIRONMENTPARAMETERSPROTO_FLOATPARAMETERSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
+DESCRIPTOR._options = None
+_ENVIRONMENTPARAMETERSPROTO_FLOATPARAMETERSENTRY._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/header_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/header.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n!communicator_objects/header.proto\x12\x14\x63ommunicator_objects\")\n\x06Header\x12\x0e\n\x06status\x18\x01 \x01(\x05\x12\x0f\n\x07message\x18\x02 \x01(\tB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
       has_default_value=False, default_value=0,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='message', full_name='communicator_objects.Header.message', index=1,
       number=2, type=9, cpp_type=9, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(Header)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/resolution_proto_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/resolution_proto.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n+communicator_objects/resolution_proto.proto\x12\x14\x63ommunicator_objects\"D\n\x0fResolutionProto\x12\r\n\x05width\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\x12\n\ngray_scale\x18\x03 \x01(\x08\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
       has_default_value=False, default_value=0,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='height', full_name='communicator_objects.ResolutionProto.height', index=1,
       number=2, type=5, cpp_type=1, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='gray_scale', full_name='communicator_objects.ResolutionProto.gray_scale', index=2,
       number=3, type=8, cpp_type=7, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(ResolutionProto)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/space_type_proto_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/space_type_proto.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n+communicator_objects/space_type_proto.proto\x12\x14\x63ommunicator_objects\x1a+communicator_objects/resolution_proto.proto*.\n\x0eSpaceTypeProto\x12\x0c\n\x08\x64iscrete\x10\x00\x12\x0e\n\ncontinuous\x10\x01\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[communicator__objects_dot_resolution__proto__pb2.DESCRIPTOR,])
   values=[
     _descriptor.EnumValueDescriptor(
       name='discrete', index=0, number=0,
-      options=None,
+      serialized_options=None,
-      options=None,
+      serialized_options=None,
-  options=None,
+  serialized_options=None,
   serialized_start=114,
   serialized_end=160,
 )
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/unity_input_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/unity_input.proto',
   package='communicator_objects',
   syntax='proto3',
-  serialized_pb=_b('\n&communicator_objects/unity_input.proto\x12\x14\x63ommunicator_objects\x1a)communicator_objects/unity_rl_input.proto\x1a\x38\x63ommunicator_objects/unity_rl_initialization_input.proto\"\xb0\x01\n\nUnityInput\x12\x34\n\x08rl_input\x18\x01 \x01(\x0b\x32\".communicator_objects.UnityRLInput\x12Q\n\x17rl_initialization_input\x18\x02 \x01(\x0b\x32\x30.communicator_objects.UnityRLInitializationInput\x12\x19\n\x11\x63ustom_data_input\x18\x03 \x01(\x05\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
+  serialized_pb=_b('\n&communicator_objects/unity_input.proto\x12\x14\x63ommunicator_objects\x1a)communicator_objects/unity_rl_input.proto\x1a\x38\x63ommunicator_objects/unity_rl_initialization_input.proto\"\x95\x01\n\nUnityInput\x12\x34\n\x08rl_input\x18\x01 \x01(\x0b\x32\".communicator_objects.UnityRLInput\x12Q\n\x17rl_initialization_input\x18\x02 \x01(\x0b\x32\x30.communicator_objects.UnityRLInitializationInputB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[communicator__objects_dot_unity__rl__input__pb2.DESCRIPTOR,communicator__objects_dot_unity__rl__initialization__input__pb2.DESCRIPTOR,])
 
       has_default_value=False, default_value=None,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='rl_initialization_input', full_name='communicator_objects.UnityInput.rl_initialization_input', index=1,
       number=2, type=11, cpp_type=10, label=1,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='custom_data_input', full_name='communicator_objects.UnityInput.custom_data_input', index=2,
-      number=3, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
-  serialized_end=342,
+  serialized_end=315,
 )
 
 _UNITYINPUT.fields_by_name['rl_input'].message_type = communicator__objects_dot_unity__rl__input__pb2._UNITYRLINPUT
 _sym_db.RegisterMessage(UnityInput)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/unity_message_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/unity_message.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n(communicator_objects/unity_message.proto\x12\x14\x63ommunicator_objects\x1a\'communicator_objects/unity_output.proto\x1a&communicator_objects/unity_input.proto\x1a!communicator_objects/header.proto\"\xac\x01\n\x0cUnityMessage\x12,\n\x06header\x18\x01 \x01(\x0b\x32\x1c.communicator_objects.Header\x12\x37\n\x0cunity_output\x18\x02 \x01(\x0b\x32!.communicator_objects.UnityOutput\x12\x35\n\x0bunity_input\x18\x03 \x01(\x0b\x32 .communicator_objects.UnityInputB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[communicator__objects_dot_unity__output__pb2.DESCRIPTOR,communicator__objects_dot_unity__input__pb2.DESCRIPTOR,communicator__objects_dot_header__pb2.DESCRIPTOR,])
       has_default_value=False, default_value=None,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='unity_output', full_name='communicator_objects.UnityMessage.unity_output', index=1,
       number=2, type=11, cpp_type=10, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='unity_input', full_name='communicator_objects.UnityMessage.unity_input', index=2,
       number=3, type=11, cpp_type=10, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(UnityMessage)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/unity_output_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/unity_output.proto',
   package='communicator_objects',
   syntax='proto3',
-  serialized_pb=_b('\n\'communicator_objects/unity_output.proto\x12\x14\x63ommunicator_objects\x1a*communicator_objects/unity_rl_output.proto\x1a\x39\x63ommunicator_objects/unity_rl_initialization_output.proto\"\xb6\x01\n\x0bUnityOutput\x12\x36\n\trl_output\x18\x01 \x01(\x0b\x32#.communicator_objects.UnityRLOutput\x12S\n\x18rl_initialization_output\x18\x02 \x01(\x0b\x32\x31.communicator_objects.UnityRLInitializationOutput\x12\x1a\n\x12\x63ustom_data_output\x18\x03 \x01(\tB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
+  serialized_pb=_b('\n\'communicator_objects/unity_output.proto\x12\x14\x63ommunicator_objects\x1a*communicator_objects/unity_rl_output.proto\x1a\x39\x63ommunicator_objects/unity_rl_initialization_output.proto\"\x9a\x01\n\x0bUnityOutput\x12\x36\n\trl_output\x18\x01 \x01(\x0b\x32#.communicator_objects.UnityRLOutput\x12S\n\x18rl_initialization_output\x18\x02 \x01(\x0b\x32\x31.communicator_objects.UnityRLInitializationOutputB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[communicator__objects_dot_unity__rl__output__pb2.DESCRIPTOR,communicator__objects_dot_unity__rl__initialization__output__pb2.DESCRIPTOR,])
 
       has_default_value=False, default_value=None,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='rl_initialization_output', full_name='communicator_objects.UnityOutput.rl_initialization_output', index=1,
       number=2, type=11, cpp_type=10, label=1,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='custom_data_output', full_name='communicator_objects.UnityOutput.custom_data_output', index=2,
-      number=3, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
-  serialized_end=351,
+  serialized_end=323,
 )
 
 _UNITYOUTPUT.fields_by_name['rl_output'].message_type = communicator__objects_dot_unity__rl__output__pb2._UNITYRLOUTPUT
 _sym_db.RegisterMessage(UnityOutput)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/unity_rl_initialization_input_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/unity_rl_initialization_input.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n8communicator_objects/unity_rl_initialization_input.proto\x12\x14\x63ommunicator_objects\"*\n\x1aUnityRLInitializationInput\x12\x0c\n\x04seed\x18\x01 \x01(\x05\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
       has_default_value=False, default_value=0,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(UnityRLInitializationInput)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/unity_rl_initialization_output_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/unity_rl_initialization_output.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n9communicator_objects/unity_rl_initialization_output.proto\x12\x14\x63ommunicator_objects\x1a\x31\x63ommunicator_objects/brain_parameters_proto.proto\x1a\x37\x63ommunicator_objects/environment_parameters_proto.proto\"\xe6\x01\n\x1bUnityRLInitializationOutput\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x10\n\x08log_path\x18\x03 \x01(\t\x12\x44\n\x10\x62rain_parameters\x18\x05 \x03(\x0b\x32*.communicator_objects.BrainParametersProto\x12P\n\x16\x65nvironment_parameters\x18\x06 \x01(\x0b\x32\x30.communicator_objects.EnvironmentParametersProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[communicator__objects_dot_brain__parameters__proto__pb2.DESCRIPTOR,communicator__objects_dot_environment__parameters__proto__pb2.DESCRIPTOR,])
       has_default_value=False, default_value=_b("").decode('utf-8'),
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='version', full_name='communicator_objects.UnityRLInitializationOutput.version', index=1,
       number=2, type=9, cpp_type=9, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='log_path', full_name='communicator_objects.UnityRLInitializationOutput.log_path', index=2,
       number=3, type=9, cpp_type=9, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='brain_parameters', full_name='communicator_objects.UnityRLInitializationOutput.brain_parameters', index=3,
       number=5, type=11, cpp_type=10, label=3,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='environment_parameters', full_name='communicator_objects.UnityRLInitializationOutput.environment_parameters', index=4,
       number=6, type=11, cpp_type=10, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(UnityRLInitializationOutput)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
+DESCRIPTOR._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/unity_rl_input_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/unity_rl_input.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n)communicator_objects/unity_rl_input.proto\x12\x14\x63ommunicator_objects\x1a-communicator_objects/agent_action_proto.proto\x1a\x37\x63ommunicator_objects/environment_parameters_proto.proto\x1a(communicator_objects/command_proto.proto\"\xb4\x03\n\x0cUnityRLInput\x12K\n\ragent_actions\x18\x01 \x03(\x0b\x32\x34.communicator_objects.UnityRLInput.AgentActionsEntry\x12P\n\x16\x65nvironment_parameters\x18\x02 \x01(\x0b\x32\x30.communicator_objects.EnvironmentParametersProto\x12\x13\n\x0bis_training\x18\x03 \x01(\x08\x12\x33\n\x07\x63ommand\x18\x04 \x01(\x0e\x32\".communicator_objects.CommandProto\x1aM\n\x14ListAgentActionProto\x12\x35\n\x05value\x18\x01 \x03(\x0b\x32&.communicator_objects.AgentActionProto\x1al\n\x11\x41gentActionsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x46\n\x05value\x18\x02 \x01(\x0b\x32\x37.communicator_objects.UnityRLInput.ListAgentActionProto:\x02\x38\x01\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[communicator__objects_dot_agent__action__proto__pb2.DESCRIPTOR,communicator__objects_dot_environment__parameters__proto__pb2.DESCRIPTOR,communicator__objects_dot_command__proto__pb2.DESCRIPTOR,])
       has_default_value=False, default_value=[],
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
       has_default_value=False, default_value=_b("").decode('utf-8'),
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='value', full_name='communicator_objects.UnityRLInput.AgentActionsEntry.value', index=1,
       number=2, type=11, cpp_type=10, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
+  serialized_options=_b('8\001'),
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
       has_default_value=False, default_value=[],
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='environment_parameters', full_name='communicator_objects.UnityRLInput.environment_parameters', index=1,
       number=2, type=11, cpp_type=10, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='is_training', full_name='communicator_objects.UnityRLInput.is_training', index=2,
       number=3, type=8, cpp_type=7, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='command', full_name='communicator_objects.UnityRLInput.command', index=3,
       number=4, type=14, cpp_type=8, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(UnityRLInput.AgentActionsEntry)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
-_UNITYRLINPUT_AGENTACTIONSENTRY.has_options = True
-_UNITYRLINPUT_AGENTACTIONSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
+DESCRIPTOR._options = None
+_UNITYRLINPUT_AGENTACTIONSENTRY._options = None
 # @@protoc_insertion_point(module_scope)

python/communicator_objects/unity_rl_output_pb2.py

 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
   name='communicator_objects/unity_rl_output.proto',
   package='communicator_objects',
   syntax='proto3',
+  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
   serialized_pb=_b('\n*communicator_objects/unity_rl_output.proto\x12\x14\x63ommunicator_objects\x1a+communicator_objects/agent_info_proto.proto\"\xa3\x02\n\rUnityRLOutput\x12\x13\n\x0bglobal_done\x18\x01 \x01(\x08\x12G\n\nagentInfos\x18\x02 \x03(\x0b\x32\x33.communicator_objects.UnityRLOutput.AgentInfosEntry\x1aI\n\x12ListAgentInfoProto\x12\x33\n\x05value\x18\x01 \x03(\x0b\x32$.communicator_objects.AgentInfoProto\x1ai\n\x0f\x41gentInfosEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x45\n\x05value\x18\x02 \x01(\x0b\x32\x36.communicator_objects.UnityRLOutput.ListAgentInfoProto:\x02\x38\x01\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[communicator__objects_dot_agent__info__proto__pb2.DESCRIPTOR,])
       has_default_value=False, default_value=[],
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
       has_default_value=False, default_value=_b("").decode('utf-8'),
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='value', full_name='communicator_objects.UnityRLOutput.AgentInfosEntry.value', index=1,
       number=2, type=11, cpp_type=10, label=1,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
+  serialized_options=_b('8\001'),
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
       has_default_value=False, default_value=False,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='agentInfos', full_name='communicator_objects.UnityRLOutput.agentInfos', index=1,
       number=2, type=11, cpp_type=10, label=3,
-      options=None, file=DESCRIPTOR),
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
-  options=None,
+  serialized_options=None,
   is_extendable=False,
   syntax='proto3',
   extension_ranges=[],
 _sym_db.RegisterMessage(UnityRLOutput.AgentInfosEntry)
 
 
-DESCRIPTOR.has_options = True
-DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
-_UNITYRLOUTPUT_AGENTINFOSENTRY.has_options = True
-_UNITYRLOUTPUT_AGENTINFOSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
+DESCRIPTOR._options = None
+_UNITYRLOUTPUT_AGENTINFOSENTRY._options = None
 # @@protoc_insertion_point(module_scope)

python/learn.py

       --slow                     Whether to run the game at training speed [default: False].
       --train                    Whether to train model, or only run inference [default: False].
       --worker-id=<n>            Number to add to communication port (5005). Used for multi-environment [default: 0].
-      --docker-target-name=<dt>       Docker Volume to store curriculum, executable and model files [default: Empty].
+      --docker-target-name=<dt>  Docker Volume to store curriculum, executable and model files [default: Empty].
+      --no-graphics              Whether to run the Unity simulator in no-graphics mode [default: False].
     '''
 
     options = docopt(_USAGE)
         curriculum_file = None
     lesson = int(options['--lesson'])
     fast_simulation = not bool(options['--slow'])
+    no_graphics = options['--no-graphics']
 
     # Constants
     # Assumption that this yaml is present in same dir as this file
     def run_training(sub_id):
         tc = TrainerController(env_path, run_id+"-"+str(sub_id), save_freq, curriculum_file, fast_simulation,
                                load_model, train_model, worker_id+sub_id, keep_checkpoints, lesson, seed,
-                               docker_target_name, TRAINER_CONFIG_PATH)
+                               docker_target_name, TRAINER_CONFIG_PATH, no_graphics)
         tc.start_learning()
 
     ray.init()

python/requirements.txt

 pytest>=3.2.2
 docopt
 pyyaml
-protobuf==3.5.2
+protobuf==3.6.0
 grpcio==1.11.0

python/tests/mock_communicator.py

 
 
 class MockCommunicator(Communicator):
-    def __init__(self, discrete=False, visual_input=False):
+    def __init__(self, discrete_action=False, visual_inputs=0):
         """
         Python side of the grpc communication. Python is the client and Unity the server
 
-        self.is_discrete = discrete
+        self.is_discrete = discrete_action
-        self.visual_input = visual_input
+        self.visual_inputs = visual_inputs
-        if self.visual_input:
-            resolutions = [ResolutionProto(
-                width=30,
-                height=40,
-                gray_scale=False)]
-        else:
-            resolutions = []
+        resolutions = [ResolutionProto(
+            width=30,
+            height=40,
+            gray_scale=False) for i in range(self.visual_inputs)]
         bp = BrainParametersProto(
             vector_observation_size=3,
             num_stacked_vector_observations=2,

python/tests/test_bc.py

     with tf.Session() as sess:
         with tf.variable_scope("FakeGraphScope"):
             mock_communicator.return_value = MockCommunicator(
-                discrete=False, visual_input=False)
+                discrete_action=False, visual_inputs=0)
             env = UnityEnvironment(' ')
             model = BehavioralCloningModel(env.brains["RealFakeBrain"])
             init = tf.global_variables_initializer()
     with tf.Session() as sess:
         with tf.variable_scope("FakeGraphScope"):
             mock_communicator.return_value = MockCommunicator(
-                discrete=True, visual_input=True)
+                discrete_action=True, visual_inputs=0)
+            env = UnityEnvironment(' ')
+            model = BehavioralCloningModel(env.brains["RealFakeBrain"])
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            run_list = [model.sample_action, model.policy]
+            feed_dict = {model.batch_size: 2,
+                         model.dropout_rate: 1.0,
+                         model.sequence_length: 1,
+                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                   [3, 4, 5, 3, 4, 5]])}
+            sess.run(run_list, feed_dict=feed_dict)
+            env.close()
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_visual_dc_bc_model(mock_communicator, mock_launcher):
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        with tf.variable_scope("FakeGraphScope"):
+            mock_communicator.return_value = MockCommunicator(
+                discrete_action=True, visual_inputs=2)
             env = UnityEnvironment(' ')
             model = BehavioralCloningModel(env.brains["RealFakeBrain"])
             init = tf.global_variables_initializer()
                          model.sequence_length: 1,
                          model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
                                                    [3, 4, 5, 3, 4, 5]]),
-                         model.visual_in[0]: np.ones([2, 40, 30, 3])}
+                         model.visual_in[0]: np.ones([2, 40, 30, 3]),
+                         model.visual_in[1]: np.ones([2, 40, 30, 3])}
+            sess.run(run_list, feed_dict=feed_dict)
+            env.close()
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_visual_cc_bc_model(mock_communicator, mock_launcher):
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        with tf.variable_scope("FakeGraphScope"):
+            mock_communicator.return_value = MockCommunicator(
+                discrete_action=False, visual_inputs=2)
+            env = UnityEnvironment(' ')
+            model = BehavioralCloningModel(env.brains["RealFakeBrain"])
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            run_list = [model.sample_action, model.policy]
+            feed_dict = {model.batch_size: 2,
+                         model.sequence_length: 1,
+                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                   [3, 4, 5, 3, 4, 5]]),
+                         model.visual_in[0]: np.ones([2, 40, 30, 3]),
+                         model.visual_in[1]: np.ones([2, 40, 30, 3])}
             sess.run(run_list, feed_dict=feed_dict)
             env.close()
 

python/tests/test_ppo.py

 import tensorflow as tf
 
 from unitytrainers.ppo.models import PPOModel
+from unitytrainers.ppo.trainer import discount_rewards
 from unityagents import UnityEnvironment
 from .mock_communicator import MockCommunicator
 
-def test_ppo_model_continuous(mock_communicator, mock_launcher):
+def test_ppo_model_cc_vector(mock_communicator, mock_launcher):
-                discrete=False, visual_input=False)
+                discrete_action=False, visual_inputs=0)
             env = UnityEnvironment(' ')
 
             model = PPOModel(env.brains["RealFakeBrain"])
             feed_dict = {model.batch_size: 2,
                          model.sequence_length: 1,
                          model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
-                                                   [3, 4, 5, 3, 4, 5]])}
+                                                    [3, 4, 5, 3, 4, 5]])}
             sess.run(run_list, feed_dict=feed_dict)
             env.close()
 
-def test_ppo_model_discrete(mock_communicator, mock_launcher):
+def test_ppo_model_cc_visual(mock_communicator, mock_launcher):
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        with tf.variable_scope("FakeGraphScope"):
+            mock_communicator.return_value = MockCommunicator(
+                discrete_action=False, visual_inputs=2)
+            env = UnityEnvironment(' ')
+
+            model = PPOModel(env.brains["RealFakeBrain"])
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            run_list = [model.output, model.probs, model.value, model.entropy,
+                        model.learning_rate]
+            feed_dict = {model.batch_size: 2,
+                         model.sequence_length: 1,
+                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                    [3, 4, 5, 3, 4, 5]]),
+                         model.visual_in[0]: np.ones([2, 40, 30, 3]),
+                         model.visual_in[1]: np.ones([2, 40, 30, 3])}
+            sess.run(run_list, feed_dict=feed_dict)
+            env.close()
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_ppo_model_dc_visual(mock_communicator, mock_launcher):
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        with tf.variable_scope("FakeGraphScope"):
+            mock_communicator.return_value = MockCommunicator(
+                discrete_action=True, visual_inputs=2)
+            env = UnityEnvironment(' ')
+            model = PPOModel(env.brains["RealFakeBrain"])
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            run_list = [model.output, model.all_probs, model.value, model.entropy,
+                        model.learning_rate]
+            feed_dict = {model.batch_size: 2,
+                         model.sequence_length: 1,
+                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                    [3, 4, 5, 3, 4, 5]]),
+                         model.visual_in[0]: np.ones([2, 40, 30, 3]),
+                         model.visual_in[1]: np.ones([2, 40, 30, 3])
+                         }
+            sess.run(run_list, feed_dict=feed_dict)
+            env.close()
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_ppo_model_dc_vector(mock_communicator, mock_launcher):
-                discrete=True, visual_input=True)
+                discrete_action=True, visual_inputs=0)
             env = UnityEnvironment(' ')
             model = PPOModel(env.brains["RealFakeBrain"])
             init = tf.global_variables_initializer()
             feed_dict = {model.batch_size: 2,
                          model.sequence_length: 1,
                          model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
-                                                   [3, 4, 5, 3, 4, 5]]),
-                         model.visual_in[0]: np.ones([2, 40, 30, 3])
+                                                    [3, 4, 5, 3, 4, 5]])}
+            sess.run(run_list, feed_dict=feed_dict)
+            env.close()
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_ppo_model_dc_vector_rnn(mock_communicator, mock_launcher):
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        with tf.variable_scope("FakeGraphScope"):
+            mock_communicator.return_value = MockCommunicator(
+                discrete_action=True, visual_inputs=0)
+            env = UnityEnvironment(' ')
+            memory_size = 128
+            model = PPOModel(env.brains["RealFakeBrain"], use_recurrent=True, m_size=memory_size)
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            run_list = [model.output, model.all_probs, model.value, model.entropy,
+                        model.learning_rate, model.memory_out]
+            feed_dict = {model.batch_size: 1,
+                         model.sequence_length: 2,
+                         model.prev_action: [0, 0],
+                         model.memory_in: np.zeros((1, memory_size)),
+                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                    [3, 4, 5, 3, 4, 5]])}
+            sess.run(run_list, feed_dict=feed_dict)
+            env.close()
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_ppo_model_cc_vector_rnn(mock_communicator, mock_launcher):
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        with tf.variable_scope("FakeGraphScope"):
+            mock_communicator.return_value = MockCommunicator(
+                discrete_action=False, visual_inputs=0)
+            env = UnityEnvironment(' ')
+            memory_size = 128
+            model = PPOModel(env.brains["RealFakeBrain"], use_recurrent=True, m_size=memory_size)
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            run_list = [model.output, model.all_probs, model.value, model.entropy,
+                        model.learning_rate, model.memory_out]
+            feed_dict = {model.batch_size: 1,
+                         model.sequence_length: 2,
+                         model.memory_in: np.zeros((1, memory_size)),
+                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                    [3, 4, 5, 3, 4, 5]])}
+            sess.run(run_list, feed_dict=feed_dict)
+            env.close()
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_ppo_model_dc_vector_curio(mock_communicator, mock_launcher):
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        with tf.variable_scope("FakeGraphScope"):
+            mock_communicator.return_value = MockCommunicator(
+                discrete_action=True, visual_inputs=0)
+            env = UnityEnvironment(' ')
+            model = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True)
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            run_list = [model.output, model.all_probs, model.value, model.entropy,
+                        model.learning_rate, model.intrinsic_reward]
+            feed_dict = {model.batch_size: 2,
+                         model.sequence_length: 1,
+                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                    [3, 4, 5, 3, 4, 5]]),
+                         model.next_vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                         [3, 4, 5, 3, 4, 5]]),
+                         model.action_holder: [0, 0]}
+            sess.run(run_list, feed_dict=feed_dict)
+            env.close()
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_ppo_model_cc_vector_curio(mock_communicator, mock_launcher):
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        with tf.variable_scope("FakeGraphScope"):
+            mock_communicator.return_value = MockCommunicator(
+                discrete_action=False, visual_inputs=0)
+            env = UnityEnvironment(' ')
+            model = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True)
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            run_list = [model.output, model.all_probs, model.value, model.entropy,
+                        model.learning_rate, model.intrinsic_reward]
+            feed_dict = {model.batch_size: 2,
+                         model.sequence_length: 1,
+                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                    [3, 4, 5, 3, 4, 5]]),
+                         model.next_vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                         [3, 4, 5, 3, 4, 5]]),
+                         model.output: [[0.0, 0.0], [0.0, 0.0]]}
+            sess.run(run_list, feed_dict=feed_dict)
+            env.close()
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_ppo_model_dc_visual_curio(mock_communicator, mock_launcher):
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        with tf.variable_scope("FakeGraphScope"):
+            mock_communicator.return_value = MockCommunicator(
+                discrete_action=True, visual_inputs=2)
+            env = UnityEnvironment(' ')
+            model = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True)
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            run_list = [model.output, model.all_probs, model.value, model.entropy,
+                        model.learning_rate, model.intrinsic_reward]
+            feed_dict = {model.batch_size: 2,
+                         model.sequence_length: 1,
+                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                    [3, 4, 5, 3, 4, 5]]),
+                         model.next_vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                         [3, 4, 5, 3, 4, 5]]),
+                         model.action_holder: [0, 0],
+                         model.visual_in[0]: np.ones([2, 40, 30, 3]),
+                         model.visual_in[1]: np.ones([2, 40, 30, 3]),
+                         model.next_visual_in[0]: np.ones([2, 40, 30, 3]),
+                         model.next_visual_in[1]: np.ones([2, 40, 30, 3])
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_ppo_model_cc_visual_curio(mock_communicator, mock_launcher):
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        with tf.variable_scope("FakeGraphScope"):
+            mock_communicator.return_value = MockCommunicator(
+                discrete_action=False, visual_inputs=2)
+            env = UnityEnvironment(' ')
+            model = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True)
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            run_list = [model.output, model.all_probs, model.value, model.entropy,
+                        model.learning_rate, model.intrinsic_reward]
+            feed_dict = {model.batch_size: 2,
+                         model.sequence_length: 1,
+                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                    [3, 4, 5, 3, 4, 5]]),
+                         model.next_vector_in: np.array([[1, 2, 3, 1, 2, 3],
+                                                         [3, 4, 5, 3, 4, 5]]),
+                         model.output: [[0.0, 0.0], [0.0, 0.0]],
+                         model.visual_in[0]: np.ones([2, 40, 30, 3]),
+                         model.visual_in[1]: np.ones([2, 40, 30, 3]),
+                         model.next_visual_in[0]: np.ones([2, 40, 30, 3]),
+                         model.next_visual_in[1]: np.ones([2, 40, 30, 3])
+                         }
+            sess.run(run_list, feed_dict=feed_dict)
+            env.close()
+
+
+def test_rl_functions():
+    rewards = np.array([0.0, 0.0, 0.0, 1.0])
+    gamma = 0.9
+    returns = discount_rewards(rewards, gamma, 0.0)
+    np.testing.assert_array_almost_equal(returns, np.array([0.729, 0.81, 0.9, 1.0]))
 
 
 if __name__ == '__main__':

python/tests/test_unityagents.py

 @mock.patch('unityagents.UnityEnvironment.get_communicator')
 def test_initialization(mock_communicator, mock_launcher):
     mock_communicator.return_value = MockCommunicator(
-        discrete=False, visual_input=False)
+        discrete_action=False, visual_inputs=0)
     env = UnityEnvironment(' ')
     with pytest.raises(UnityActionException):
         env.step([0])
 @mock.patch('unityagents.UnityEnvironment.get_communicator')
 def test_reset(mock_communicator, mock_launcher):
     mock_communicator.return_value = MockCommunicator(
-        discrete=False, visual_input=False)
+        discrete_action=False, visual_inputs=0)
     env = UnityEnvironment(' ')
     brain = env.brains['RealFakeBrain']
     brain_info = env.reset()
 @mock.patch('unityagents.UnityEnvironment.get_communicator')
 def test_step(mock_communicator, mock_launcher):
     mock_communicator.return_value = MockCommunicator(
-        discrete=False, visual_input=False)
+        discrete_action=False, visual_inputs=0)
     env = UnityEnvironment(' ')
     brain = env.brains['RealFakeBrain']
     brain_info = env.reset()
 @mock.patch('unityagents.UnityEnvironment.get_communicator')
 def test_close(mock_communicator, mock_launcher):
     comm = MockCommunicator(
-        discrete=False, visual_input=False)
+        discrete_action=False, visual_inputs=0)
     mock_communicator.return_value = comm
     env = UnityEnvironment(' ')
     assert env._loaded

python/tests/test_unitytrainers.py

     summary_freq: 1000
     use_recurrent: false
     memory_size: 8
+    use_curiosity: false
+    curiosity_strength: 0.0
+    curiosity_enc_size: 1
 ''')
 
 dummy_bc_config = yaml.load('''
     summary_freq: 1000
     use_recurrent: false
     memory_size: 8
+    use_curiosity: false
+    curiosity_strength: 0.0
+    curiosity_enc_size: 1
 ''')
 
 dummy_bad_config = yaml.load('''
 @mock.patch('unityagents.UnityEnvironment.get_communicator')
 def test_initialization(mock_communicator, mock_launcher):
     mock_communicator.return_value = MockCommunicator(
-        discrete=True, visual_input=True)
+        discrete_action=True, visual_inputs=1)
-                           1, 1, 1, '', "tests/test_unitytrainers.py")
+                           1, 1, 1, '', "tests/test_unitytrainers.py", False)
     assert(tc.env.brain_names[0] == 'RealFakeBrain')
 
 
         with mock.patch(open_name, create=True) as _:
             mock_load.return_value = dummy_config
             mock_communicator.return_value = MockCommunicator(
-                discrete=True, visual_input=True)
+                discrete_action=True, visual_inputs=1)
-                                       1, 1, 1, '','')
+                                       1, 1, 1, '','', False)
             config = tc._load_config()
             assert(len(config) == 1)
             assert(config['default']['trainer'] == "ppo")
     with mock.patch('yaml.load') as mock_load:
         with mock.patch(open_name, create=True) as _:
             mock_communicator.return_value = MockCommunicator(
-                discrete=True, visual_input=True)
+                discrete_action=True, visual_inputs=1)
-                                   1, 1, 1, '', "tests/test_unitytrainers.py")
+                                   1, 1, 1, '', "tests/test_unitytrainers.py", False)
 
             # Test for PPO trainer
             mock_load.return_value = dummy_config
             )
             b[fake_agent_id]['action'].append([100 * fake_agent_id + 10 * step + 4,
                                                100 * fake_agent_id + 10 * step + 5])
-    a = b[1]['vector_observation'].get_batch(batch_size=2, training_length=None, sequential=True)
+    a = b[1]['vector_observation'].get_batch(batch_size=2, training_length=1, sequential=True)
     assert_array(a, np.array([[171, 172, 173], [181, 182, 183]]))
     a = b[2]['vector_observation'].get_batch(batch_size=2, training_length=3, sequential=True)
     assert_array(a, np.array([

python/trainer_config.yaml

     gamma: 0.995
     beta: 0.001
 
-BouncerBrain:
-    normalize: true
-    max_steps: 5.0e5
-    num_layers: 2
-    hidden_units: 56
-
 TennisBrain:
     normalize: true
 
     gamma: 0.995
     max_steps: 1e6
     summary_freq: 3000
+    num_layers: 3
+    hidden_units: 512
 
 WalkerBrain:
     normalize: true
     buffer_size: 20480
-    beta: 0.001
-    num_layers: 2
-    hidden_units: 256
+    num_layers: 3
+    hidden_units: 512
 
 ReacherBrain:
     normalize: true
     batches_per_epoch: 5
     num_layers: 4
     hidden_units: 64
-    use_recurrent: false
     sequence_length: 16
     buffer_size: 128
 

python/unityagents/brain.py

 
 class BrainInfo:
     def __init__(self, visual_observation, vector_observation, text_observations, memory=None,
-                reward=None, agents=None, local_done=None,
-                vector_action=None, text_action=None, max_reached=None):
+                 reward=None, agents=None, local_done=None,
+                 vector_action=None, text_action=None, max_reached=None):
         """
         Describes experience at current step of all agents linked to a brain.
         """
         self.vector_action_space_size = brain_param["vectorActionSize"]
         self.vector_action_descriptions = brain_param["vectorActionDescriptions"]
         self.vector_action_space_type = ["discrete", "continuous"][brain_param["vectorActionSpaceType"]]
-        self.vector_observation_space_type = ["discrete", "continuous"][brain_param["vectorObservationSpaceType"]]
-        return '''Unity brain name: {0}
-        Number of Visual Observations (per agent): {1}
-        Vector Observation space type: {2}
-        Vector Observation space size (per agent): {3}
-        Number of stacked Vector Observation: {4}
-        Vector Action space type: {5}
-        Vector Action space size (per agent): {6}
-        Vector Action descriptions: {7}'''.format(self.brain_name,
-                                           str(self.number_visual_observations),
-                                           self.vector_observation_space_type,
-                                           str(self.vector_observation_space_size),
-                                           str(self.num_stacked_vector_observations),
-                                           self.vector_action_space_type,
-                                           str(self.vector_action_space_size),
-                                           ', '.join(self.vector_action_descriptions))
+        return '''Unity brain name: {}
+        Number of Visual Observations (per agent): {}
+        Vector Observation space size (per agent): {}
+        Number of stacked Vector Observation: {}
+        Vector Action space type: {}
+        Vector Action space size (per agent): {}
+        Vector Action descriptions: {}'''.format(self.brain_name,
+                                                 str(self.number_visual_observations),
+                                                 str(self.vector_observation_space_size),
+                                                 str(self.num_stacked_vector_observations),
+                                                 self.vector_action_space_type,
+                                                 str(self.vector_action_space_size),
+                                                 ', '.join(self.vector_action_descriptions))

python/unityagents/communicator.py

 
 
 class Communicator(object):
-    def __init__(self, worker_id=0,
-                 base_port=5005):
+    def __init__(self, worker_id=0, base_port=5005):
         """
         Python side of the communication. Must be used in pair with the right Unity Communicator equivalent.
 

python/unityagents/environment.py

 class UnityEnvironment(object):
     def __init__(self, file_name=None, worker_id=0,
                  base_port=5005, curriculum=None,
-                 seed=0, docker_training=False):
+                 seed=0, docker_training=False, no_graphics=False):
         """
         Starts a new unity environment and establishes a connection with the environment.
         Notice: Currently communication between Unity and Python takes place over an open socket without authentication.
         :int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
         :int worker_id: Number to add to communication port (5005) [0]. Used for asynchronous agent scenarios.
         :param docker_training: Informs this class whether the process is being run within a container.
+        :param no_graphics: Whether to run the Unity simulator in no-graphics mode
         """
 
         atexit.register(self._close)
         self._loaded = False    # If true, this means the environment was successfully loaded
         self.proc1 = None       # The process that is started. If None, no process was started
-
-            self.executable_launcher(file_name, docker_training)
+            self.executable_launcher(file_name, docker_training, no_graphics)
-            logger.info("Ready to connect with the Editor.")
+            logger.info("Start training by pressing the Play button in the Unity Editor.")
         self._loaded = True
 
         rl_init_parameters_in = UnityRLInitializationInput(
                     "cameraResolutions": resolution,
                     "vectorActionSize": brain_param.vector_action_size,
                     "vectorActionDescriptions": brain_param.vector_action_descriptions,
-                    "vectorActionSpaceType": brain_param.vector_action_space_type,
-                    "vectorObservationSpaceType": brain_param.vector_observation_space_type
+                    "vectorActionSpaceType": brain_param.vector_action_space_type
                 })
             if brain_param.brain_type == 2:
                 self._external_brain_names += [brain_param.brain_name]
     def external_brain_names(self):
         return self._external_brain_names
 
-    def executable_launcher(self, file_name, docker_training):
+    def executable_launcher(self, file_name, docker_training, no_graphics):
         cwd = os.getcwd()
         file_name = (file_name.strip()
                      .replace('.app', '').replace('.exe', '').replace('.x86_64', '').replace('.x86', ''))
             logger.debug("This is the launch string {}".format(launch_string))
             # Launch Unity environment
             if not docker_training:
-                self.proc1 = subprocess.Popen(
-                    [launch_string,
-                     '--port', str(self.port)])
+                if no_graphics:
+                    self.proc1 = subprocess.Popen(
+                        [launch_string,'-nographics', '-batchmode',
+                         '--port', str(self.port)])
+                else:
+                    self.proc1 = subprocess.Popen(
+                        [launch_string, '--port', str(self.port)])
             else:
                 """
                 Comments for future maintenance:
             agent_info_list = output.agentInfos[b].value
             vis_obs = []
             for i in range(self.brains[b].number_visual_observations):
-                obs = [
-                    self._process_pixels(x.visual_observations[i], self.brains[b].camera_resolutions[i]['blackAndWhite'])
+                obs = [self._process_pixels(x.visual_observations[i],
+                                            self.brains[b].camera_resolutions[i]['blackAndWhite'])
-            memory_size = max([len(x.memories) for x in agent_info_list])
+            if len(agent_info_list) == 0:
+                memory_size = 0
+            else:
+                memory_size = max([len(x.memories) for x in agent_info_list])
-                memory = np.zeros((0,0))
+                memory = np.zeros((0, 0))
             else:
                 [x.memories.extend([0] * (memory_size - len(x.memories))) for x in agent_info_list]
                 memory = np.array([x.memories for x in agent_info_list])

python/unitytrainers/bc/models.py

         if self.use_recurrent:
             tf.Variable(self.m_size, name="memory_size", trainable=False, dtype=tf.int32)
             self.memory_in = tf.placeholder(shape=[None, self.m_size], dtype=tf.float32, name='recurrent_in')
-            hidden_reg, self.memory_out = self.create_recurrent_encoder(hidden_reg, self.memory_in)
+            hidden_reg, self.memory_out = self.create_recurrent_encoder(hidden_reg, self.memory_in,
+                                                                        self.sequence_length)
-        self.policy = tf.layers.dense(hidden_reg, self.a_size, activation=None, use_bias=False,
+        self.policy = tf.layers.dense(hidden_reg, self.a_size, activation=None, use_bias=False, name='pre_action',
                                       kernel_initializer=c_layers.variance_scaling_initializer(factor=0.01))
 
         if brain.vector_action_space_type == "discrete":

python/unitytrainers/bc/trainer.py

         self.brain_to_imitate = trainer_parameters['brain_to_imitate']
         self.batches_per_epoch = trainer_parameters['batches_per_epoch']
         self.use_recurrent = trainer_parameters['use_recurrent']
-        self.step = 0
         self.sequence_length = 1
         self.m_size = None
         if self.use_recurrent:
 
         self.training_buffer = Buffer()
         self.is_continuous_action = (env.brains[brain_name].vector_action_space_type == "continuous")
-        self.is_continuous_observation = (env.brains[brain_name].vector_observation_space_type == "continuous")
         self.use_observations = (env.brains[brain_name].number_visual_observations > 0)
         if self.use_observations:
             logger.info('Cannot use observations with imitation learning')
                 normalize=False,
                 use_recurrent=trainer_parameters['use_recurrent'],
                 brain=self.brain)
+        self.inference_run_list = [self.model.sample_action]
+        if self.use_recurrent:
+            self.inference_run_list += [self.model.memory_out]
 
     def __str__(self):
 
         Returns the number of steps the trainer has performed
         :return: the step count of the trainer
         """
-        return self.step
+        return self.sess.run(self.model.global_step)
 
     @property
     def get_last_reward(self):
         else:
             return 0
 
-    def increment_step(self):
+    def increment_step_and_update_last_reward(self):
-        Increment the step count of the trainer
+        Increment the step count of the trainer and Updates the last reward
-        self.step += 1
-
-    def update_last_reward(self):
-        """
-        Updates the last reward
-        """
+        self.sess.run(self.model.increment_step)
         return
 
     def take_action(self, all_brain_info: AllBrainInfo):
 
         agent_brain = all_brain_info[self.brain_name]
         feed_dict = {self.model.dropout_rate: 1.0, self.model.sequence_length: 1}
-        run_list = [self.model.sample_action]
+
         if self.use_observations:
             for i, _ in enumerate(agent_brain.visual_observations):
                 feed_dict[self.model.visual_in[i]] = agent_brain.visual_observations[i]
             if agent_brain.memories.shape[1] == 0:
                 agent_brain.memories = np.zeros((len(agent_brain.agents), self.m_size))
             feed_dict[self.model.memory_in] = agent_brain.memories
-            run_list += [self.model.memory_out]
-            agent_action, memories = self.sess.run(run_list, feed_dict)
+            agent_action, memories = self.sess.run(self.inference_run_list, feed_dict)
-            agent_action = self.sess.run(run_list, feed_dict)
+            agent_action = self.sess.run(self.inference_run_list, feed_dict)
         return agent_action, None, None, None
 
     def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo, take_action_outputs):
         for l in range(len(info_student.agents)):
             if info_student.local_done[l]:
                 agent_id = info_student.agents[l]
-                self.stats['cumulative_reward'].append(self.cumulative_rewards[agent_id])
-                self.stats['episode_length'].append(self.episode_steps[agent_id])
+                self.stats['cumulative_reward'].append(
+                    self.cumulative_rewards.get(agent_id, 0))
+                self.stats['episode_length'].append(
+                    self.episode_steps.get(agent_id, 0))
                 self.cumulative_rewards[agent_id] = 0
                 self.episode_steps[agent_id] = 0
 
                 feed_dict[self.model.true_action] = batch_actions.reshape([-1, self.brain.vector_action_space_size])
             else:
                 feed_dict[self.model.true_action] = batch_actions.reshape([-1])
-            if not self.is_continuous_observation:
-                feed_dict[self.model.vector_in] = batch_states.reshape([-1, self.brain.num_stacked_vector_observations])
-            else:
-                feed_dict[self.model.vector_in] = batch_states.reshape([-1, self.brain.vector_observation_space_size *
-                                                                       self.brain.num_stacked_vector_observations])
+            feed_dict[self.model.vector_in] = batch_states.reshape([-1, self.brain.vector_observation_space_size *
+                                                                   self.brain.num_stacked_vector_observations])
             if self.use_observations:
                 for i, _ in enumerate(self.model.visual_in):
                     _obs = np.array(_buffer['visual_observations%d' % i][start:end])

python/unitytrainers/buffer.py

 
         class AgentBufferField(list):
             """
-            AgentBufferField is a list of numpy arrays. When an agent collects a field, you can add it to his 
+            AgentBufferField is a list of numpy arrays. When an agent collects a field, you can add it to his
             AgentBufferField with the append method.
             """
 
                 self[:] = []
                 self[:] = list(np.array(data))
 
-            def get_batch(self, batch_size=None, training_length=None, sequential=True):
+            def get_batch(self, batch_size=None, training_length=1, sequential=True):
-                :param batch_size: The number of elements to retrieve. If None: 
+                :param batch_size: The number of elements to retrieve. If None:
-                :param sequential: If true and training_length is not None: the elements 
+                :param sequential: If true and training_length is not None: the elements
-                if training_length is None:
-                    # When the training length is None, the method returns a list of elements,
+                if training_length == 1:
+                    # When the training length is 1, the method returns a list of elements,
                     # not a list of sequences of elements.
                     if batch_size is None:
                         # If batch_size is None : All the elements of the AgentBufferField are returned.
         def check_length(self, key_list):
             """
             Some methods will require that some fields have the same length.
-            check_length will return true if the fields in key_list 
+            check_length will return true if the fields in key_list
             have the same length.
             :param key_list: The fields which length will be compared
             """

python/unitytrainers/models.py

         self.visual_in = []
         self.batch_size = tf.placeholder(shape=None, dtype=tf.int32, name='batch_size')
         self.sequence_length = tf.placeholder(shape=None, dtype=tf.int32, name='sequence_length')
+        self.mask_input = tf.placeholder(shape=[None], dtype=tf.float32, name='masks')
+        self.mask = tf.cast(self.mask_input, tf.int32)
         self.m_size = m_size
         self.normalize = normalize
         self.use_recurrent = use_recurrent
         :param o_size: Size of stacked vector observation.
         :return:
         """
-        if self.brain.vector_observation_space_type == "continuous":
-            self.vector_in = tf.placeholder(shape=[None, self.o_size], dtype=tf.float32, name=name)
-            if self.normalize:
-                self.running_mean = tf.get_variable("running_mean", [self.o_size], trainable=False, dtype=tf.float32,
-                                                    initializer=tf.zeros_initializer())
-                self.running_variance = tf.get_variable("running_variance", [self.o_size], trainable=False,
-                                                        dtype=tf.float32, initializer=tf.ones_initializer())
-                self.new_mean = tf.placeholder(shape=[self.o_size], dtype=tf.float32, name='new_mean')
-                self.new_variance = tf.placeholder(shape=[self.o_size], dtype=tf.float32, name='new_variance')
-                self.update_mean = tf.assign(self.running_mean, self.new_mean)
-                self.update_variance = tf.assign(self.running_variance, self.new_variance)
+        self.vector_in = tf.placeholder(shape=[None, self.o_size], dtype=tf.float32, name=name)
+        if self.normalize:
+            self.running_mean = tf.get_variable("running_mean", [self.o_size], trainable=False, dtype=tf.float32,
+                                                initializer=tf.zeros_initializer())
+            self.running_variance = tf.get_variable("running_variance", [self.o_size], trainable=False,
+                                                    dtype=tf.float32, initializer=tf.ones_initializer())
+            self.update_mean, self.update_variance = self.create_normalizer_update(self.vector_in)
-                self.normalized_state = tf.clip_by_value((self.vector_in - self.running_mean) / tf.sqrt(
-                    self.running_variance / (tf.cast(self.global_step, tf.float32) + 1)), -5, 5,
-                                                         name="normalized_state")
-                return self.normalized_state
-            else:
-                return self.vector_in
+            self.normalized_state = tf.clip_by_value((self.vector_in - self.running_mean) / tf.sqrt(
+                self.running_variance / (tf.cast(self.global_step, tf.float32) + 1)), -5, 5,
+                                                     name="normalized_state")
+            return self.normalized_state
-            self.vector_in = tf.placeholder(shape=[None, 1], dtype=tf.int32, name='vector_observation')
+    def create_normalizer_update(self, vector_input):
+        mean_current_observation = tf.reduce_mean(vector_input, axis=0)
+        new_mean = self.running_mean + (mean_current_observation - self.running_mean) / \
+                   tf.cast(self.global_step + 1, tf.float32)
+        new_variance = self.running_variance + (mean_current_observation - new_mean) * \
+                       (mean_current_observation - self.running_mean)
+        update_mean = tf.assign(self.running_mean, new_mean)
+        update_variance = tf.assign(self.running_variance, new_variance)
+        return update_mean, update_variance
+
-    def create_continuous_observation_encoder(observation_input, h_size, activation, num_layers, scope, reuse):
+    def create_vector_observation_encoder(observation_input, h_size, activation, num_layers, scope, reuse):
         """
         Builds a set of hidden state encoders.
         :param reuse: Whether to re-use the weights within the same scope.
                                      activation=tf.nn.elu, reuse=reuse, name="conv_2")
             hidden = c_layers.flatten(conv2)
 
-        hidden_flat = self.create_continuous_observation_encoder(hidden, h_size, activation, num_layers, scope, reuse)
+        with tf.variable_scope(scope+'/'+'flat_encoding'):
+            hidden_flat = self.create_vector_observation_encoder(hidden, h_size, activation,
+                                                                 num_layers, scope, reuse)
-    @staticmethod
-    def create_discrete_observation_encoder(observation_input, s_size, h_size, activation,
-                                            num_layers, scope, reuse):
-        """
-        Builds a set of hidden state encoders from discrete state input.
-        :param reuse: Whether to re-use the weights within the same scope.
-        :param scope: The scope of the graph within which to create the ops.
-        :param observation_input: Discrete observation.
-        :param s_size: state input size (discrete).
-        :param h_size: Hidden layer size.
-        :param activation: What type of activation function to use for layers.
-        :param num_layers: number of hidden layers to create.
-        :return: List of hidden layer tensors.
-        """
-        with tf.name_scope(scope):
-            vector_in = tf.reshape(observation_input, [-1])
-            state_onehot = tf.one_hot(vector_in, s_size)
-            hidden = state_onehot
-            for i in range(num_layers):
-                hidden = tf.layers.dense(hidden, h_size, use_bias=False, activation=activation,
-                                         reuse=reuse, name="hidden_{}".format(i))
-        return hidden
-
     def create_observation_streams(self, num_streams, h_size, num_layers):
         """
         Creates encoding stream for observations.
                 for j in range(brain.number_visual_observations):
                     encoded_visual = self.create_visual_observation_encoder(self.visual_in[j], h_size,
                                                                             activation_fn, num_layers,
-                                                                            "main_graph_{}".format(i), False)
+                                                                            "main_graph_{}_encoder{}"
+                                                                            .format(i, j), False)
-                if brain.vector_observation_space_type == "continuous":
-                    hidden_state = self.create_continuous_observation_encoder(vector_observation_input,
-                                                                              h_size, activation_fn, num_layers,
-                                                                              "main_graph_{}".format(i), False)
-                else:
-                    hidden_state = self.create_discrete_observation_encoder(vector_observation_input, self.o_size,
-                                                                            h_size, activation_fn, num_layers,
-                                                                            "main_graph_{}".format(i), False)
+                hidden_state = self.create_vector_observation_encoder(vector_observation_input,
+                                                                      h_size, activation_fn, num_layers,
+                                                                          "main_graph_{}".format(i), False)
             if hidden_state is not None and hidden_visual is not None:
                 final_hidden = tf.concat([hidden_visual, hidden_state], axis=1)
             elif hidden_state is None and hidden_visual is not None:

python/unitytrainers/ppo/models.py

 
                 # Create the encoder ops for current and next visual input. Not that these encoders are siamese.
                 encoded_visual = self.create_visual_observation_encoder(self.visual_in[i], self.curiosity_enc_size,
-                                                                        self.swish, 1, "visual_obs_encoder", False)
-                encoded_next_visual = self.create_visual_observation_encoder(self.next_visual_in[i], self.curiosity_enc_size,
-                                                                             self.swish, 1, "visual_obs_encoder", True)
+                                                                        self.swish, 1, "stream_{}_visual_obs_encoder"
+                                                                        .format(i), False)
+
+                encoded_next_visual = self.create_visual_observation_encoder(self.next_visual_in[i],
+                                                                             self.curiosity_enc_size,
+                                                                             self.swish, 1,
+                                                                             "stream_{}_visual_obs_encoder".format(i),
+                                                                             True)
                 visual_encoders.append(encoded_visual)
                 next_visual_encoders.append(encoded_next_visual)
 
             encoded_next_state_list.append(hidden_next_visual)
 
         if self.o_size > 0:
-            # Create input op for next (t+1) vector observation.
-            self.next_vector_obs = tf.placeholder(shape=[None, self.o_size], dtype=tf.float32,
-                                                  name='next_vector_observation')
-
-            encoded_vector_obs = self.create_continuous_observation_encoder(self.vector_in, self.curiosity_enc_size,
-                                                                            self.swish, 2, "vector_obs_encoder", False)
-            encoded_next_vector_obs = self.create_continuous_observation_encoder(self.next_vector_obs,
-                                                                                 self.curiosity_enc_size, self.swish,
-                                                                                 2, "vector_obs_encoder", True)
+            # Create input op for next (t+1) vector observation.
+            self.next_vector_in = tf.placeholder(shape=[None, self.o_size], dtype=tf.float32,
+                                                 name='next_vector_observation')
+            encoded_vector_obs = self.create_vector_observation_encoder(self.vector_in,
+                                                                        self.curiosity_enc_size,
+                                                                        self.swish, 2, "vector_obs_encoder",
+                                                                        False)
+            encoded_next_vector_obs = self.create_vector_observation_encoder(self.next_vector_in,
+                                                                             self.curiosity_enc_size,
+                                                                             self.swish, 2,
+                                                                                 "vector_obs_encoder",
+                                                                             True)
             encoded_state_list.append(encoded_vector_obs)
             encoded_next_state_list.append(encoded_next_vector_obs)
 
         if self.brain.vector_action_space_type == "continuous":
             pred_action = tf.layers.dense(hidden, self.a_size, activation=None)
             squared_difference = tf.reduce_sum(tf.squared_difference(pred_action, self.selected_actions), axis=1)
-            self.inverse_loss = tf.reduce_mean(squared_difference)
+            self.inverse_loss = tf.reduce_mean(tf.dynamic_partition(squared_difference, self.mask, 2)[1])
-            self.inverse_loss = tf.reduce_mean(cross_entropy)
+            self.inverse_loss = tf.reduce_mean(tf.dynamic_partition(cross_entropy, self.mask, 2)[1])
 
     def create_forward_model(self, encoded_state, encoded_next_state):
         """
         """
         combined_input = tf.concat([encoded_state, self.selected_actions], axis=1)
         hidden = tf.layers.dense(combined_input, 256, activation=self.swish)
-        pred_next_state = tf.layers.dense(hidden, self.curiosity_enc_size, activation=None)
+        # We compare against the concatenation of all observation streams, hence `self.v_size + int(self.o_size > 0)`.
+        pred_next_state = tf.layers.dense(hidden, self.curiosity_enc_size * (self.v_size + int(self.o_size > 0)),
+                                          activation=None)
-        self.forward_loss = tf.reduce_mean(squared_difference)
+        self.forward_loss = tf.reduce_mean(tf.dynamic_partition(squared_difference, self.mask, 2)[1])
 
     def create_ppo_optimizer(self, probs, old_probs, value, entropy, beta, epsilon, lr, max_step):
         """
         self.learning_rate = tf.train.polynomial_decay(lr, self.global_step, max_step, 1e-10, power=1.0)
 
         self.old_value = tf.placeholder(shape=[None], dtype=tf.float32, name='old_value_estimates')
-        self.mask_input = tf.placeholder(shape=[None], dtype=tf.float32, name='masks')
-        mask = tf.equal(self.mask_input, 1.0)
-
-        self.value_loss = tf.reduce_mean(tf.boolean_mask(tf.maximum(v_opt_a, v_opt_b), mask))
+        self.value_loss = tf.reduce_mean(tf.dynamic_partition(tf.maximum(v_opt_a, v_opt_b), self.mask, 2)[1])
 
         # Here we calculate PPO policy loss. In continuous control this is done independently for each action gaussian
         # and then averaged together. This provides significantly better performance than treating the probability
         p_opt_b = tf.clip_by_value(r_theta, 1.0 - decay_epsilon, 1.0 + decay_epsilon) * self.advantage
-        self.policy_loss = -tf.reduce_mean(tf.boolean_mask(tf.minimum(p_opt_a, p_opt_b), mask))
+        self.policy_loss = -tf.reduce_mean(tf.dynamic_partition(tf.minimum(p_opt_a, p_opt_b), self.mask, 2)[1])
-            tf.boolean_mask(entropy, mask))
+            tf.dynamic_partition(entropy, self.mask, 2)[1])
+
         if self.use_curiosity:
             self.loss += 10 * (0.2 * self.forward_loss + 0.8 * self.inverse_loss)
         self.update_batch = optimizer.minimize(self.loss)

python/unitytrainers/ppo/trainer.py

 # # Unity ML Agents
 # ## ML-Agent Learning (PPO)
-# Contains an implementation of PPO as described [here](https://arxiv.org/abs/1707.06347).
+# Contains an implementation of PPO as described (https://arxiv.org/abs/1707.06347).
 
 import logging
 import os
         self.use_recurrent = trainer_parameters["use_recurrent"]
         self.use_curiosity = bool(trainer_parameters['use_curiosity'])
         self.sequence_length = 1
+        self.step = 0
-        if self.use_recurrent:
             if self.m_size == 0:
                 raise UnityTrainerException("The memory size for brain {0} is 0 even though the trainer uses recurrent."
                                             .format(brain_name))
         self.cumulative_rewards = {}
         self.episode_steps = {}
         self.is_continuous_action = (env.brains[brain_name].vector_action_space_type == "continuous")
-        self.is_continuous_observation = (env.brains[brain_name].vector_observation_space_type == "continuous")
         self.use_visual_obs = (env.brains[brain_name].number_visual_observations > 0)
         self.use_vector_obs = (env.brains[brain_name].vector_observation_space_size > 0)
         self.summary_path = trainer_parameters['summary_path']
         self.summary_writer = tf.summary.FileWriter(self.summary_path)
 
+        self.inference_run_list = [self.model.output, self.model.all_probs, self.model.value,
+                                   self.model.entropy, self.model.learning_rate]
+        if self.is_continuous_action:
+            self.inference_run_list.append(self.model.output_pre)
+        if self.use_recurrent:
+            self.inference_run_list.extend([self.model.memory_out])
+        if self.is_training and self.use_vector_obs and self.trainer_parameters['normalize']:
+            self.inference_run_list.extend([self.model.update_mean, self.model.update_variance])
+
-        return '''Hypermarameters for the PPO Trainer of brain {0}: \n{1}'''.format(
+        return '''Hyperparameters for the PPO Trainer of brain {0}: \n{1}'''.format(
             self.brain_name, '\n'.join(['\t{0}:\t{1}'.format(x, self.trainer_parameters[x]) for x in self.param_keys]))
 
     @property
         Returns the number of steps the trainer has performed
         :return: the step count of the trainer
         """
-        return self.sess.run(self.model.global_step)
+        return self.step
 
     @property
     def get_last_reward(self):
         """
         return self.sess.run(self.model.last_reward)
 
-    def increment_step(self):
-        """
-        Increment the step count of the trainer
-        """
-        self.sess.run(self.model.increment_step)
-
-    def update_last_reward(self):
+    def increment_step_and_update_last_reward(self):
-        Updates the last reward
+        Increment the step count of the trainer and Updates the last reward
-            self.sess.run(self.model.update_reward, feed_dict={self.model.new_reward: mean_reward})
-
-    def running_average(self, data, steps, running_mean, running_variance):
-        """
-        Computes new running mean and variances.
-        :param data: New piece of data.
-        :param steps: Total number of data so far.
-        :param running_mean: TF op corresponding to stored running mean.
-        :param running_variance: TF op corresponding to stored running variance.
-        :return: New mean and variance values.
-        """
-        mean, var = self.sess.run([running_mean, running_variance])
-        current_x = np.mean(data, axis=0)
-        new_mean = mean + (current_x - mean) / (steps + 1)
-        new_variance = var + (current_x - new_mean) * (current_x - mean)
-        return new_mean, new_variance
+            self.sess.run([self.model.update_reward,
+                           self.model.increment_step],
+                          feed_dict={self.model.new_reward: mean_reward})
+        else:
+            self.sess.run(self.model.increment_step)
+        self.step = self.sess.run(self.model.global_step)
 
     def take_action(self, all_brain_info: AllBrainInfo):
         """
         to be passed to add experiences
         """
-        steps = self.get_step
-        feed_dict = {self.model.batch_size: len(curr_brain_info.vector_observations), self.model.sequence_length: 1}
-        run_list = [self.model.output, self.model.all_probs, self.model.value, self.model.entropy,
-                    self.model.learning_rate]
-        if self.is_continuous_action:
-            run_list.append(self.model.output_pre)
+
+        feed_dict = {self.model.batch_size: len(curr_brain_info.vector_observations),
+                     self.model.sequence_length: 1}
-            feed_dict[self.model.prev_action] = np.reshape(curr_brain_info.previous_vector_actions, [-1])
+            if not self.is_continuous_action:
+                feed_dict[self.model.prev_action] = curr_brain_info.previous_vector_actions.flatten()
-            run_list += [self.model.memory_out]
-        if (self.is_training and self.is_continuous_observation and
-                self.use_vector_obs and self.trainer_parameters['normalize']):
-            new_mean, new_variance = self.running_average(
-                curr_brain_info.vector_observations, steps, self.model.running_mean, self.model.running_variance)
-            feed_dict[self.model.new_mean] = new_mean
-            feed_dict[self.model.new_variance] = new_variance
-            run_list = run_list + [self.model.update_mean, self.model.update_variance]
+
+        values = self.sess.run(self.inference_run_list, feed_dict=feed_dict)
+        run_out = dict(zip(self.inference_run_list, values))
-        values = self.sess.run(run_list, feed_dict=feed_dict)
-        run_out = dict(zip(run_list, values))
-            return (run_out[self.model.output],
-                    run_out[self.model.memory_out],
-                    [str(v) for v in run_out[self.model.value]],
-                    run_out)
+            return run_out[self.model.output], run_out[self.model.memory_out], None, run_out
-            return (run_out[self.model.output],
-                    None,
-                    [str(v) for v in run_out[self.model.value]],
-                    run_out)
+            return run_out[self.model.output], None, None, run_out
-    def add_experiences(self, curr_all_info: AllBrainInfo, next_all_info: AllBrainInfo, take_action_outputs):
+    def generate_intrinsic_rewards(self, curr_info, next_info):
-        Adds experiences to each agent's experience history.
-        :param curr_all_info: Dictionary of all current brains and corresponding BrainInfo.
-        :param next_all_info: Dictionary of all current brains and corresponding BrainInfo.
-        :param take_action_outputs: The outputs of the take action method.
+        Generates intrinsic reward used for Curiosity-based training.
+        :param curr_info: Current BrainInfo.
+        :param next_info: Next BrainInfo.
+        :return: Intrinsic rewards for all agents.
-        curr_info = curr_all_info[self.brain_name]
-        next_info = next_all_info[self.brain_name]
-
-        intrinsic_rewards = np.array([])
+            if curr_info.agents != next_info.agents:
+                raise UnityTrainerException("Training with Curiosity-driven exploration"
+                                            " and On-Demand Decision making is currently not supported.")
-            run_list = [self.model.intrinsic_reward]
-                run_list.append(self.model.output)
+                feed_dict[self.model.output] = next_info.previous_vector_actions
-                feed_dict[self.model.action_holder] = np.reshape(take_action_outputs[self.model.output], [-1])
+                feed_dict[self.model.action_holder] = next_info.previous_vector_actions.flatten()
-                for i, _ in enumerate(curr_info.visual_observations):
+                for i in range(len(curr_info.visual_observations)):
-                feed_dict[self.model.next_vector_obs] = next_info.vector_observations
+                feed_dict[self.model.next_vector_in] = next_info.vector_observations
-                feed_dict[self.model.prev_action] = np.reshape(curr_info.previous_vector_actions, [-1])
-                run_list += [self.model.memory_out]
+            intrinsic_rewards = self.sess.run(self.model.intrinsic_reward,
+                                              feed_dict=feed_dict) * float(self.has_updated)
+            return intrinsic_rewards
+        else:
+            return None
-            intrinsic_rewards = self.sess.run(self.model.intrinsic_reward, feed_dict=feed_dict) * \
-                                float(self.has_updated)
+    def generate_value_estimate(self, brain_info, idx):
+        """
+        Generates value estimates for bootstrapping.
+        :param brain_info: BrainInfo to be used for bootstrapping.
+        :param idx: Index in BrainInfo of agent.
+        :return: Value estimate.
+        """
+        feed_dict = {self.model.batch_size: 1, self.model.sequence_length: 1}
+        if self.use_visual_obs:
+            for i in range(len(brain_info.visual_observations)):
+                feed_dict[self.model.visual_in[i]] = [brain_info.visual_observations[i][idx]]
+        if self.use_vector_obs:
+            feed_dict[self.model.vector_in] = [brain_info.vector_observations[idx]]
+        if self.use_recurrent:
+            if brain_info.memories.shape[1] == 0:
+                brain_info.memories = np.zeros(
+                    (len(brain_info.vector_observations), self.m_size))
+            feed_dict[self.model.memory_in] = [brain_info.memories[idx]]
+        if not self.is_continuous_action and self.use_recurrent:
+            feed_dict[self.model.prev_action] = brain_info.previous_vector_actions[idx].flatten()
+        value_estimate = self.sess.run(self.model.value, feed_dict)
+        return value_estimate
+
+    def add_experiences(self, curr_all_info: AllBrainInfo, next_all_info: AllBrainInfo, take_action_outputs):
+        """
+        Adds experiences to each agent's experience history.
+        :param curr_all_info: Dictionary of all current brains and corresponding BrainInfo.
+        :param next_all_info: Dictionary of all current brains and corresponding BrainInfo.
+        :param take_action_outputs: The outputs of the take action method.
+        """
+        curr_info = curr_all_info[self.brain_name]
+        next_info = next_all_info[self.brain_name]
+
+        intrinsic_rewards = self.generate_intrinsic_rewards(curr_info, next_info)
 
         for agent_id in curr_info.agents:
             self.training_buffer[agent_id].last_brain_info = curr_info
             stored_info = self.training_buffer[agent_id].last_brain_info
             stored_take_action_outputs = self.training_buffer[agent_id].last_take_action_outputs
-            if stored_info is None:
-                continue
-            else:
+            if stored_info is not None:
                 idx = stored_info.agents.index(agent_id)
                 next_idx = next_info.agents.index(agent_id)
                 if not stored_info.local_done[idx]:
                                 next_info.visual_observations[i][idx])
                     if self.use_vector_obs:
                         self.training_buffer[agent_id]['vector_obs'].append(stored_info.vector_observations[idx])
-                        self.training_buffer[agent_id]['next_vector_obs'].append(
+                        self.training_buffer[agent_id]['next_vector_in'].append(
                             next_info.vector_observations[next_idx])
                     if self.use_recurrent:
                         if stored_info.memories.shape[1] == 0:
                     else:
                         bootstrapping_info = info
                         idx = l
-                    feed_dict = {self.model.batch_size: len(bootstrapping_info.vector_observations),
-                                 self.model.sequence_length: 1}
-                    if self.use_visual_obs:
-                        for i in range(len(bootstrapping_info.visual_observations)):
-                            feed_dict[self.model.visual_in[i]] = bootstrapping_info.visual_observations[i]
-                    if self.use_vector_obs:
-                        feed_dict[self.model.vector_in] = bootstrapping_info.vector_observations
-                    if self.use_recurrent:
-                        if bootstrapping_info.memories.shape[1] == 0:
-                            bootstrapping_info.memories = np.zeros(
-                                (len(bootstrapping_info.vector_observations), self.m_size))
-                        feed_dict[self.model.memory_in] = bootstrapping_info.memories
-                    if not self.is_continuous_action and self.use_recurrent:
-                        feed_dict[self.model.prev_action] = np.reshape(bootstrapping_info.previous_vector_actions, [-1])
-                    value_next = self.sess.run(self.model.value, feed_dict)[idx]
+                    value_next = self.generate_value_estimate(bootstrapping_info, idx)
 
                 self.training_buffer[agent_id]['advantages'].set(
                     get_gae(
                         gamma=self.trainer_parameters['gamma'],
-                        lambd=self.trainer_parameters['lambd'])
-                )
+                        lambd=self.trainer_parameters['lambd']))
-                self.training_buffer.append_update_buffer(agent_id,
-                                                          batch_size=None, training_length=self.sequence_length)
+                self.training_buffer.append_update_buffer(agent_id, batch_size=None,
+                                                          training_length=self.sequence_length)
-                    self.stats['cumulative_reward'].append(self.cumulative_rewards[agent_id])
-                    self.stats['episode_length'].append(self.episode_steps[agent_id])
+                    self.stats['cumulative_reward'].append(
+                        self.cumulative_rewards.get(agent_id, 0))
+                    self.stats['episode_length'].append(
+                        self.episode_steps.get(agent_id, 0))
-                        self.stats['intrinsic_reward'].append(self.intrinsic_rewards[agent_id])
+                        self.stats['intrinsic_reward'].append(
+                            self.intrinsic_rewards.get(agent_id, 0))
                         self.intrinsic_rewards[agent_id] = 0
 
     def end_episode(self):
         Returns whether or not the trainer has enough elements to run update model
         :return: A boolean corresponding to whether or not update_model() can be run
         """
-        return len(self.training_buffer.update_buffer['actions']) > \
-               max(int(self.trainer_parameters['buffer_size'] / self.sequence_length), 1)
+        size_of_buffer = len(self.training_buffer.update_buffer['actions'])
+        return size_of_buffer > max(int(self.trainer_parameters['buffer_size'] / self.sequence_length), 1)
-        num_epoch = self.trainer_parameters['num_epoch']
+        num_epoch = self.trainer_parameters['num_epoch']
+            buffer = self.training_buffer.update_buffer
-                _buffer = self.training_buffer.update_buffer
-                             self.model.mask_input: np.array(_buffer['masks'][start:end]).reshape(
-                                 [-1]),
-                             self.model.returns_holder: np.array(_buffer['discounted_returns'][start:end]).reshape(
-                                 [-1]),
-                             self.model.old_value: np.array(_buffer['value_estimates'][start:end]).reshape([-1]),
-                             self.model.advantage: np.array(_buffer['advantages'][start:end]).reshape([-1, 1]),
-                             self.model.all_old_probs: np.array(
-                                 _buffer['action_probs'][start:end]).reshape([-1, self.brain.vector_action_space_size])}
+                             self.model.mask_input: np.array(buffer['masks'][start:end]).flatten(),
+                             self.model.returns_holder: np.array(buffer['discounted_returns'][start:end]).flatten(),
+                             self.model.old_value: np.array(buffer['value_estimates'][start:end]).flatten(),
+                             self.model.advantage: np.array(buffer['advantages'][start:end]).reshape([-1, 1]),
+                             self.model.all_old_probs: np.array(buffer['action_probs'][start:end]).reshape(
+                                 [-1, self.brain.vector_action_space_size])}
-                    feed_dict[self.model.output_pre] = np.array(
-                        _buffer['actions_pre'][start:end]).reshape([-1, self.brain.vector_action_space_size])
+                    feed_dict[self.model.output_pre] = np.array(buffer['actions_pre'][start:end]).reshape(
+                        [-1, self.brain.vector_action_space_size])
-                    feed_dict[self.model.action_holder] = np.array(
-                        _buffer['actions'][start:end]).reshape([-1])
+                    feed_dict[self.model.action_holder] = np.array(buffer['actions'][start:end]).flatten()
-                        feed_dict[self.model.prev_action] = np.array(
-                            _buffer['prev_action'][start:end]).reshape([-1])
+                        feed_dict[self.model.prev_action] = np.array(buffer['prev_action'][start:end]).flatten()
-                    if self.is_continuous_observation:
-                        feed_dict[self.model.vector_in] = np.array(
-                            _buffer['vector_obs'][start:end]).reshape(
-                            [-1, self.brain.vector_observation_space_size * self.brain.num_stacked_vector_observations])
-                        if self.use_curiosity:
-                            feed_dict[self.model.next_vector_obs] = np.array(
-                                _buffer['next_vector_obs'][start:end]).reshape(
-                                [-1,
-                                 self.brain.vector_observation_space_size * self.brain.num_stacked_vector_observations])
-                    else:
-                        feed_dict[self.model.vector_in] = np.array(
-                            _buffer['vector_obs'][start:end]).reshape([-1, self.brain.num_stacked_vector_observations])
+                    total_observation_length = self.brain.vector_observation_space_size * \
+                                               self.brain.num_stacked_vector_observations
+                    feed_dict[self.model.vector_in] = np.array(buffer['vector_obs'][start:end]).reshape(
+                        [-1, total_observation_length])
+                    if self.use_curiosity:
+                        feed_dict[self.model.next_vector_in] = np.array(buffer['next_vector_in'][start:end]) \
+                            .reshape([-1, total_observation_length])
-                        _obs = np.array(_buffer['visual_obs%d' % i][start:end])
-                        (_batch, _seq, _w, _h, _c) = _obs.shape
-                        feed_dict[self.model.visual_in[i]] = _obs.reshape([-1, _w, _h, _c])
+                        _obs = np.array(buffer['visual_obs%d' % i][start:end])
+                        if self.sequence_length > 1 and self.use_recurrent:
+                            (_batch, _seq, _w, _h, _c) = _obs.shape
+                            feed_dict[self.model.visual_in[i]] = _obs.reshape([-1, _w, _h, _c])
+                        else:
+                            feed_dict[self.model.visual_in[i]] = _obs
-                            _obs = np.array(_buffer['next_visual_obs%d' % i][start:end])
-                            (_batch, _seq, _w, _h, _c) = _obs.shape
-                            feed_dict[self.model.next_visual_in[i]] = _obs.reshape([-1, _w, _h, _c])
+                            _obs = np.array(buffer['next_visual_obs%d' % i][start:end])
+                            if self.sequence_length > 1 and self.use_recurrent:
+                                (_batch, _seq, _w, _h, _c) = _obs.shape
+                                feed_dict[self.model.next_visual_in[i]] = _obs.reshape([-1, _w, _h, _c])
+                            else:
+                                feed_dict[self.model.next_visual_in[i]] = _obs
-                    mem_in = np.array(_buffer['memory'][start:end])[:, 0, :]
+                    mem_in = np.array(buffer['memory'][start:end])[:, 0, :]
                     feed_dict[self.model.memory_in] = mem_in
 
                 run_list = [self.model.value_loss, self.model.policy_loss, self.model.update_batch]
             self.stats['forward_loss'].append(np.mean(forward_total))
             self.stats['inverse_loss'].append(np.mean(inverse_total))
         self.training_buffer.reset_update_buffer()
+
 
 def discount_rewards(r, gamma=0.99, value_next=0.0):
     """

python/unitytrainers/trainer.py

         """
         raise UnityTrainerException("The get_last_reward property was not implemented.")
 
-    def increment_step(self):
-        """
-        Increment the step count of the trainer
-        """
-        raise UnityTrainerException("The increment_step method was not implemented.")
-
-    def update_last_reward(self):
+    def increment_step_and_update_last_reward(self):
-        Updates the last reward
+        Increment the step count of the trainer and updates the last reward
-        raise UnityTrainerException("The update_last_reward method was not implemented.")
+        raise UnityTrainerException("The increment_step_and_update_last_reward method was not implemented.")
 
     def take_action(self, all_brain_info: AllBrainInfo):
         """
         """
         if (self.get_step % self.trainer_parameters['summary_freq'] == 0 and self.get_step != 0 and
                 self.is_training and self.get_step <= self.get_max_steps):
-            steps = self.get_step
-                logger.info("{} {}: Step: {}. Mean Reward: {:0.3f}. Std of Reward: {:0.3f}."
-                            .format(self.run_id, self.brain_name, steps,
+                logger.info(" {}: Step: {}. Mean Reward: {:0.3f}. Std of Reward: {:0.3f}."
+                            .format(self.brain_name, self.get_step,
-                            .format(self.brain_name, steps))
+                            .format(self.brain_name, self.get_step))
             summary = tf.Summary()
             for key in self.stats:
                 if len(self.stats[key]) > 0:
             summary.value.add(tag='Info/Lesson', simple_value=lesson_number)
-            self.summary_writer.add_summary(summary, steps)
+            self.summary_writer.add_summary(summary, self.get_step)
             self.summary_writer.flush()
 
     def write_tensorboard_text(self, key, input_dict):
         :param input_dict: A dictionary that will be displayed in a table on Tensorboard.
         """
         try:
-            s_op = tf.summary.text(key,
-                                   tf.convert_to_tensor(([[str(x), str(input_dict[x])] for x in input_dict]))
-                                   )
+            s_op = tf.summary.text(key, tf.convert_to_tensor(([[str(x), str(input_dict[x])] for x in input_dict])))
             s = self.sess.run(s_op)
             self.summary_writer.add_summary(s, self.get_step)
         except:

python/unitytrainers/trainer_controller.py

 
 class TrainerController(object):
     def __init__(self, env_path, run_id, save_freq, curriculum_file, fast_simulation, load, train,
-                 worker_id, keep_checkpoints, lesson, seed, docker_target_name, trainer_config_path):
+                 worker_id, keep_checkpoints, lesson, seed, docker_target_name, trainer_config_path,
+                 no_graphics):
-
         :param env_path: Location to the environment executable to be loaded.
         :param run_id: The sub-directory name for model and summary statistics
         :param save_freq: Frequency at which to save model
         :param seed: Random seed used for training.
         :param docker_target_name: Name of docker volume that will contain all data.
         :param trainer_config_path: Fully qualified path to location of trainer configuration file
+        :param no_graphics: Whether to run the Unity simulator in no-graphics mode
         """
         self.trainer_config_path = trainer_config_path
         if env_path is not None:
             self.model_path = '/{docker_target_name}/models/{run_id}'.format(
                 docker_target_name=docker_target_name,
                 run_id=run_id)
-            if env_path is not None :
+            if env_path is not None:
                 env_path = '/{docker_target_name}/{env_name}'.format(docker_target_name=docker_target_name,
                                                                      env_name=env_path)
             if curriculum_file is None:
         tf.set_random_seed(self.seed)
         self.env = UnityEnvironment(file_name=env_path, worker_id=self.worker_id,
                                     curriculum=self.curriculum_file, seed=self.seed,
-                                    docker_training=self.docker_training)
+                                    docker_training=self.docker_training,
+                                    no_graphics=no_graphics)
         if env_path is None:
             self.env_name = 'editor_'+self.env.academy_name
         else:
                         # Write training statistics to Tensorboard.
                         trainer.write_summary(self.env.curriculum.lesson_number)
                         if self.train_model and trainer.get_step <= trainer.get_max_steps:
-                            trainer.increment_step()
-                            trainer.update_last_reward()
-                    if self.train_model and trainer.get_step <= trainer.get_max_steps:
+                            trainer.increment_step_and_update_last_reward()
+                    if self.train_model:
                         global_step += 1
                     if global_step % self.save_freq == 0 and global_step != 0 and self.train_model:
                         # Save Tensorflow model
                 if global_step != 0 and self.train_model:
                     self._save_model(sess, steps=global_step, saver=saver)
             except KeyboardInterrupt:
+                print('--------------------------Now saving model-------------------------')
                 if self.train_model:
                     self.logger.info("Learning was interrupted. Please wait while the graph is generated.")
                     self._save_model(sess, steps=global_step, saver=saver)

unity-environment/Assets/ML-Agents/Editor/BrainEditor.cs

                 EditorGUILayout.LabelField("Vector Observation");
                 EditorGUI.indentLevel++;
 
-                SerializedProperty bpVectorObsType =
-                    serializedBrain.FindProperty("brainParameters.vectorObservationSpaceType");
-                EditorGUILayout.PropertyField(bpVectorObsType, new GUIContent("Space Type",
-                    "Corresponds to whether state " +
-                    "vector contains a single integer (Discrete) " +
-                    "or a series of real-valued floats (Continuous)."));
-
                 SerializedProperty bpVectorObsSize =
                     serializedBrain.FindProperty("brainParameters.vectorObservationSize");
                 EditorGUILayout.PropertyField(bpVectorObsSize, new GUIContent("Space Size",
             EditorGUI.indentLevel = indentLevel;
             SerializedProperty bt = serializedBrain.FindProperty("brainType");
             EditorGUILayout.PropertyField(bt);
-
-
-
 
             if (bt.enumValueIndex < 0)
             {

unity-environment/Assets/ML-Agents/Examples/BananaCollectors/Prefabs/TeachingArea.prefab

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unity-environment/Assets/ML-Agents/Examples/Basic/Scripts/BasicAgent.cs

     private BasicAcademy academy;
     public float timeBetweenDecisionsAtInference;
     private float timeSinceDecision;
-    public int position;
-    public int smallGoalPosition;
-    public int largeGoalPosition;
+    int position;
+    int smallGoalPosition;
+    int largeGoalPosition;
-    public int minPosition;
-    public int maxPosition;
+    int minPosition;
+    int maxPosition;
 
     public override void InitializeAgent()
     {
     public override void CollectObservations()
     {
-        AddVectorObs(position);
+        AddVectorObs(position, 20);
     }
 
     public override void AgentAction(float[] vectorAction, string textAction)
         if (position < minPosition) { position = minPosition; }
         if (position > maxPosition) { position = maxPosition; }
 
-        gameObject.transform.position = new Vector3(position, 0f, 0f);
+        gameObject.transform.position = new Vector3(position - 10f, 0f, 0f);
 
         AddReward(-0.01f);
 
 
     public override void AgentReset()
     {
-        position = 0;
-        minPosition = -10;
-        maxPosition = 10;
-        smallGoalPosition = -3;
-        largeGoalPosition = 7;
-        smallGoal.transform.position = new Vector3(smallGoalPosition, 0f, 0f);
-        largeGoal.transform.position = new Vector3(largeGoalPosition, 0f, 0f);
+        position = 10;
+        minPosition = 0;
+        maxPosition = 20;
+        smallGoalPosition = 7;
+        largeGoalPosition = 17;
+        smallGoal.transform.position = new Vector3(smallGoalPosition - 10f, 0f, 0f);
+        largeGoal.transform.position = new Vector3(largeGoalPosition - 10f, 0f, 0f);
     }
 
     public override void AgentOnDone()

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unity-environment/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs

 
     void Awake()
     {
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-        brain = FindObjectOfType<Brain>();
         academy = FindObjectOfType<PushBlockAcademy>(); //cache the academy
     }
 

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