Merge pull request #885 from Unity-Technologies/release-v0.4

Release v0.4

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

 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

  
 ### 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 and either continuous feature vector or discrete state observations at the same time.
  
 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.  
 

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

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

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
 

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:

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/tests/mock_communicator.py

 
 
 class MockCommunicator(Communicator):
-    def __init__(self, discrete=False, visual_inputs=0):
+    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.steps = 0
         self.visual_inputs = visual_inputs
         self.has_been_closed = False

python/tests/test_bc.py

     with tf.Session() as sess:
         with tf.variable_scope("FakeGraphScope"):
             mock_communicator.return_value = MockCommunicator(
-                discrete=False, visual_inputs=0)
+                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_inputs=0)
+                discrete_action=True, 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_inputs=2)
+                discrete_action=True, visual_inputs=2)
             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=False, visual_inputs=2)
+                discrete_action=False, visual_inputs=2)
             env = UnityEnvironment(' ')
             model = BehavioralCloningModel(env.brains["RealFakeBrain"])
             init = tf.global_variables_initializer()

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_inputs=0)
+                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()
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+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()
 
-def test_ppo_model_discrete(mock_communicator, mock_launcher):
+def test_ppo_model_dc_visual(mock_communicator, mock_launcher):
-                discrete=True, visual_inputs=2)
+                discrete_action=True, visual_inputs=2)
             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]]),
+                                                    [3, 4, 5, 3, 4, 5]]),
+
+
+@mock.patch('unityagents.UnityEnvironment.executable_launcher')
+@mock.patch('unityagents.UnityEnvironment.get_communicator')
+def test_ppo_model_dc_vector(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"])
+            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]])}
+            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])
+                         }
+            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_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_inputs=0)
+        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_inputs=0)
+        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_inputs=0)
+        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_inputs=0)
+        discrete_action=False, visual_inputs=0)
     mock_communicator.return_value = comm
     env = UnityEnvironment(' ')
     assert env._loaded

python/tests/test_unitytrainers.py

 @mock.patch('unityagents.UnityEnvironment.get_communicator')
 def test_initialization(mock_communicator, mock_launcher):
     mock_communicator.return_value = MockCommunicator(
-        discrete=True, visual_inputs=1)
+        discrete_action=True, visual_inputs=1)
     tc = TrainerController(' ', ' ', 1, None, True, True, False, 1,
                            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_inputs=1)
+                discrete_action=True, visual_inputs=1)
             mock_load.return_value = dummy_config
             tc = TrainerController(' ', ' ', 1, None, True, True, False, 1,
                                        1, 1, 1, '','', False)
     with mock.patch('yaml.load') as mock_load:
         with mock.patch(open_name, create=True) as _:
             mock_communicator.return_value = MockCommunicator(
-                discrete=True, visual_inputs=1)
+                discrete_action=True, visual_inputs=1)
             tc = TrainerController(' ', ' ', 1, None, True, True, False, 1,
                                    1, 1, 1, '', "tests/test_unitytrainers.py", False)
 

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
 
     batches_per_epoch: 5
     num_layers: 4
     hidden_units: 64
-    use_recurrent: false
     sequence_length: 16
     buffer_size: 128
 

python/unityagents/environment.py

         if file_name is not None:
             self.executable_launcher(file_name, docker_training, no_graphics)
         else:
-            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(

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 num_layers: number of hidden layers to create.
         :return: List of hidden layer tensors.
         """
-        with tf.name_scope(scope):
+        with tf.variable_scope(scope):
             vector_in = tf.reshape(observation_input, [-1])
             state_onehot = tf.one_hot(vector_in, s_size)
             hidden = state_onehot

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)
+            if self.brain.vector_observation_space_type == "continuous":
+                # 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_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_in,
+                                                                                     self.curiosity_enc_size,
+                                                                                     self.swish, 2,
+                                                                                     "vector_obs_encoder",
+                                                                                     True)
+            else:
+                self.next_vector_in = tf.placeholder(shape=[None, 1], dtype=tf.int32,
+                                                     name='next_vector_observation')
+
+                encoded_vector_obs = self.create_discrete_observation_encoder(self.vector_in, self.o_size,
+                                                                              self.curiosity_enc_size,
+                                                                              self.swish, 2, "vector_obs_encoder",
+                                                                              False)
+                encoded_next_vector_obs = self.create_discrete_observation_encoder(self.next_vector_in, self.o_size,
+                                                                                   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
         feed_dict = {self.model.batch_size: len(curr_brain_info.vector_observations),
                      self.model.sequence_length: 1}
         if self.use_recurrent:
-            feed_dict[self.model.prev_action] = curr_brain_info.previous_vector_actions.flatten()
+            if not self.is_continuous_action:
+                feed_dict[self.model.prev_action] = curr_brain_info.previous_vector_actions.flatten()
             if curr_brain_info.memories.shape[1] == 0:
                 curr_brain_info.memories = np.zeros((len(curr_brain_info.agents), self.m_size))
             feed_dict[self.model.memory_in] = curr_brain_info.memories
         :return: Intrinsic rewards for all agents.
         """
         if self.use_curiosity:
+            if curr_info.agents != next_info.agents:
+                raise UnityTrainerException("Training with Curiosity-driven exploration"
+                                            " and On-Demand Decision making is currently not supported.")
-                feed_dict[self.model.output] = next_info.previous_vector_actions.flatten()
+                feed_dict[self.model.output] = next_info.previous_vector_actions
             else:
                 feed_dict[self.model.action_holder] = next_info.previous_vector_actions.flatten()
             if self.use_visual_obs:
             if self.use_vector_obs:
                 feed_dict[self.model.vector_in] = curr_info.vector_observations
-                feed_dict[self.model.next_vector_obs] = next_info.vector_observations
-
+                feed_dict[self.model.next_vector_in] = next_info.vector_observations
+            if self.use_recurrent:
+                if curr_info.memories.shape[1] == 0:
+                    curr_info.memories = np.zeros((len(curr_info.agents), self.m_size))
+                feed_dict[self.model.memory_in] = curr_info.memories
             intrinsic_rewards = self.sess.run(self.model.intrinsic_reward,
                                               feed_dict=feed_dict) * float(self.has_updated)
             return intrinsic_rewards
                                 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:
                                  [-1, self.brain.vector_action_space_size])}
                 if self.is_continuous_action:
                     feed_dict[self.model.output_pre] = np.array(buffer['actions_pre'][start:end]).reshape(
-                            [-1, self.brain.vector_action_space_size])
+                        [-1, self.brain.vector_action_space_size])
                 else:
                     feed_dict[self.model.action_holder] = np.array(buffer['actions'][start:end]).flatten()
                     if self.use_recurrent:
                         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_obs] = np.array(buffer['next_vector_obs'][start:end])\
+                            feed_dict[self.model.next_vector_in] = np.array(buffer['next_vector_in'][start:end]) \
-                                [-1, self.brain.num_stacked_vector_observations])
+                            [-1, self.brain.num_stacked_vector_observations])
+                        if self.use_curiosity:
+                            feed_dict[self.model.next_vector_in] = np.array(buffer['next_vector_in'][start:end]) \
+                                .reshape([-1, self.brain.num_stacked_vector_observations])
                 if self.use_visual_obs:
                     for i, _ in enumerate(self.model.visual_in):
                         _obs = np.array(buffer['visual_obs%d' % i][start:end])

python/unitytrainers/trainer_controller.py

                 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)

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unity-environment/Assets/ML-Agents/Examples/Bouncer/Scripts/BouncerAgent.cs

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unity-environment/Assets/ML-Agents/Examples/Crawler/Scenes/CrawlerStaticTarget.unity

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unity-environment/Assets/ML-Agents/Examples/Hallway/Scenes/VisualHallway.unity

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

 
     void Awake()
     {
-        // There is one brain in the scene so this should find our brain.
-        brain = FindObjectOfType<Brain>();
         academy = FindObjectOfType<PushBlockAcademy>(); //cache the academy
     }
 

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unity-environment/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs

     }
 
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unity-environment/Assets/ML-Agents/Scripts/CoreBrainInternal.cs

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         }

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docs/Migrating.md

+# Migrating from ML-Agents v0.3 to ML-Agents v0.4
+
+## Unity API
+ * `using MLAgents;` needs to be added in all of the C# scripts that use ML-Agents. 
+
+# Migrating from ML-Agents v0.2 to ML-Agents v0.3
+
+There are a large number of new features and improvements in ML-Agents v0.3 which change both the training process and Unity API in ways which will cause incompatibilities with environments made using older versions. This page is designed to highlight those changes for users familiar with v0.1 or v0.2 in order to ensure a smooth transition.
+
+## Important
+ * ML-Agents is no longer compatible with Python 2. 
+
+## Python Training
+ * The training script `ppo.py` and `PPO.ipynb` Python notebook have been replaced with a single `learn.py` script as the launching point for training with ML-Agents. For more information on using `learn.py`, see [here]().
+ * Hyperparameters for training brains are now stored in the `trainer_config.yaml` file. For more information on using this file, see [here]().
+
+## Unity API
+ * Modifications to an Agent's rewards must now be done using either `AddReward()` or `SetReward()`.
+ * Setting an Agent to done now requires the use of the `Done()` method.
+ * `CollectStates()` has been replaced by `CollectObservations()`, which now no longer returns a list of floats.
+ * To collect observations, call `AddVectorObs()` within `CollectObservations()`. Note that you can call `AddVectorObs()` with floats, integers, lists and arrays of floats, Vector3 and Quaternions. 
+ * `AgentStep()` has been replaced by `AgentAction()`.
+ * `WaitTime()` has been removed.
+ * The `Frame Skip` field of the Academy is replaced by the Agent's `Decision Frequency` field, enabling agent to make decisions at different frequencies.
+ * The names of the inputs in the Internal Brain have been changed. You must replace `state` with `vector_observation` and `observation` with `visual_observation`. In addition, you must remove the `epsilon` placeholder.
+
+## Semantics
+In order to more closely align with the terminology used in the Reinforcement Learning field, and to be more descriptive, we have changed the names of some of the concepts used in ML-Agents. The changes are highlighted in the table below.
+
+| Old - v0.2 and earlier | New - v0.3 and later |
+| --- | --- |
+| State | Vector Observation |
+| Observation | Visual Observation |
+| Action | Vector Action |
+| N/A | Text Observation |
+| N/A | Text Action |

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docs/Migrating-v0.3.md

-# Migrating to ML-Agents v0.3
-
-There are a large number of new features and improvements in ML-Agents v0.3 which change both the training process and Unity API in ways which will cause incompatibilities with environments made using older versions. This page is designed to highlight those changes for users familiar with v0.1 or v0.2 in order to ensure a smooth transition.
-
-## Important
- * ML-Agents is no longer compatible with Python 2. 
-
-## Python Training
- * The training script `ppo.py` and `PPO.ipynb` Python notebook have been replaced with a single `learn.py` script as the launching point for training with ML-Agents. For more information on using `learn.py`, see [here]().
- * Hyperparameters for training brains are now stored in the `trainer_config.yaml` file. For more information on using this file, see [here]().
-
-## Unity API
- * Modifications to an Agent's rewards must now be done using either `AddReward()` or `SetReward()`.
- * Setting an Agent to done now requires the use of the `Done()` method.
- * `CollectStates()` has been replaced by `CollectObservations()`, which now no longer returns a list of floats.
- * To collect observations, call `AddVectorObs()` within `CollectObservations()`. Note that you can call `AddVectorObs()` with floats, integers, lists and arrays of floats, Vector3 and Quaternions. 
- * `AgentStep()` has been replaced by `AgentAction()`.
- * `WaitTime()` has been removed.
- * The `Frame Skip` field of the Academy is replaced by the Agent's `Decision Frequency` field, enabling agent to make decisions at different frequencies.
- * The names of the inputs in the Internal Brain have been changed. You must replace `state` with `vector_observation` and `observation` with `visual_observation`. In addition, you must remove the `epsilon` placeholder.
-
-## Semantics
-In order to more closely align with the terminology used in the Reinforcement Learning field, and to be more descriptive, we have changed the names of some of the concepts used in ML-Agents. The changes are highlighted in the table below.
-
-| Old - v0.2 and earlier | New - v0.3 and later |
-| --- | --- |
-| State | Vector Observation |
-| Observation | Visual Observation |
-| Action | Vector Action |
-| N/A | Text Observation |
-| N/A | Text Action |

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