resolved merge conflict with dev-0.3 branch

README.md

 to the wider research and game developer communities. 
 
 ## Features
-* Unity Engine flexibility and simplicity
-* Flexible single-agent and multi-agent support
-* Multiple visual observations (cameras)
-* Discrete and continuous action spaces
+* Unity environment control from Python
+* 10+ sample Unity environments
+* Support for multiple environment configurations and training scenarios
+* Train memory-enhanced Agents using deep reinforcement learning
-* Built-in support for Imitation Learning (coming soon)
+* Built-in support for Imitation Learning
+* Flexible Agent control with On Demand Decision Making
-* Python control interface
-* TensorFlow Sharp Agent Embedding _[Experimental]_
+* Simplified set-up with Docker _(Experimental)_
-For more information on ML-Agents, in addition to installation, and usage
-instructions, see our [documentation home](docs). 
+**For more information, in addition to installation and usage
+instructions, see our [documentation home](docs/README.md).**
 
 We have also published a series of blog posts that are relevant for ML-Agents:
 - Overviewing reinforcement learning concepts
-- [Post]() announcing the winners of our
+- [Post](https://blogs.unity3d.com/2018/02/28/introducing-the-winners-of-the-first-ml-agents-challenge/) announcing the winners of our
 [first ML-Agents Challenge](https://connect.unity.com/challenges/ml-agents-1)
 - [Post](https://blogs.unity3d.com/2018/01/23/designing-safer-cities-through-simulations/)
 overviewing how Unity can be leveraged as a simulator to design safer cities.
 - [A Game Developer Learns Machine Learning](https://mikecann.co.uk/machine-learning/a-game-developer-learns-machine-learning-intent/)
-- [Unity3D Machine Learning – Setting up the environment & TensorFlow for AgentML on Windows 10](https://unity3d.college/2017/10/25/machine-learning-in-unity3d-setting-up-the-environment-tensorflow-for-agentml-on-windows-10/)
 - [Explore Unity Technologies ML-Agents Exclusively on Intel Architecture](https://software.intel.com/en-us/articles/explore-unity-technologies-ml-agents-exclusively-on-intel-architecture)
 
 ## Community and Feedback

docs/Background-Jupyter.md

-# Jupyter
+# Background: Jupyter
-embedded visualizations. We provide one such notebook, `Basics.ipynb`, for 
-testing the Python API. 
+embedded visualizations. We provide one such notebook, `python/Basics.ipynb`, 
+for testing the Python control interface to a Unity build. This notebook is 
+introduced in the 
+[Getting Started with the 3D Balance Ball Environment](Getting-Started-with-Balance-Ball.md)
+tutorial, but can be used for testing the connection to any Unity build.
 
 For a walkthrough of how to use Jupyter, see
 [Running the Jupyter Notebook](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html)

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

-# Getting Started with the 3D Balance Ball Example
+# Getting Started with the 3D Balance Ball Environment
 
 This tutorial walks through the end-to-end process of opening an ML-Agents 
 example environment in Unity, building the Unity executable, training an agent 
 environments or as ways to test new ML algorithms. After reading this tutorial, 
 you should be able to explore and build the example environments.
 
-![Balance Ball](images/balance.png)
+![3D Balance Ball](images/balance.png)
 
 This walk-through uses the **3D Balance Ball** environment. 3D Balance Ball contains 
 a number of platforms and balls (which are all copies of each other). 
 In order to install and set up ML-Agents, the Python dependencies and Unity, 
 see the [installation instructions](Installation.md).
 
-## Understanding a Unity Environment (Balance Ball)
+## Understanding a Unity Environment (3D Balance Ball)
 
 An agent is an autonomous actor that observes and interacts with an 
 _environment_. In the context of Unity, an environment is a scene containing 
  
 The first thing you may notice after opening the 3D Balance Ball scene is that 
 it contains not one, but several platforms.  Each platform in the scene is an 
-independent agent, but they all share the same brain.  Balance Ball does this 
+independent agent, but they all share the same brain. 3D Balance Ball does this 
 to speed up training since all twelve agents contribute to training in parallel.
 
 ### Academy
 ## Training the Brain with Reinforcement Learning
 
 Now that we have a Unity executable containing the simulation environment, we 
-can perform the training. 
+can perform the training. To first ensure that your environment and the Python 
+API work as expected, you can use the `python/Basics` 
+[Jupyter notebook](Background-Jupyter.md). 
+This notebook contains a simple walkthrough of the functionality of the API. 
+Within `Basics`, be sure to set `env_name` to the name of the environment file 
+you built earlier.
 
 ### Training with PPO
 
 
 * Lesson - only interesting when performing
 [curriculum training](Training-Curriculum-Learning.md). 
-This is not used in the 3d Balance Ball environment. 
+This is not used in the 3D Balance Ball environment. 
 * Cumulative Reward - The mean cumulative episode reward over all agents. 
 Should increase during a successful training session.
 * Entropy - How random the decisions of the model are. Should slowly decrease 

docs/Learning-Environment-Design-Brains.md

 # Brains
 
-The Brain encapsulates the decision making process. Brain objects must be children of the Academy in the Unity scene hierarchy. Every Agent must be assigned a Brain, but you can use the same Brain with more than one Agent. 
+The Brain encapsulates the decision making process. Brain objects must be children of the Academy in the Unity scene hierarchy. Every Agent must be assigned a Brain, but you can use the same Brain with more than one Agent. You can also create several Brains, attach each of the Brain to one or more than one Agent.  
 
 Use the Brain class directly, rather than a subclass. Brain behavior is determined by the **Brain Type**. ML-Agents defines four Brain Types:
 

docs/Learning-Environment-Examples.md

     * Visual Observations: 0
 * Reset Parameters: None
 
-## 3DBall
+## 3DBall: 3D Balance Ball
-![Balance Ball](images/balance.png)
+![3D Balance Ball](images/balance.png)
 
 * Set-up: A balance-ball task, where the agent controls the platform. 
 * Goal: The agent must balance the platform in order to keep the ball on it for as long as possible.
 
 * Set-up: A platforming environment where the agent can jump over a wall.
 * Goal: The agent must use the block to scale the wall and reach the goal.
-* Agents: The environment contains one agent linked to a single brain.
+* Agents: The environment contains one agent linked to two different brains. The brain the agent is linked to changes depending on the height of the wall.
-    * -0.01 for every step.
+    * -0.0005 for every step.
-    * Vector Action space: (Discrete) Size of 6, corresponding to movement in cardinal directions, jumping, and no movement.
+    * 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.
-* Reset Parameters: One, corresponding to number of steps in training. Used to adjust size of the wall for Curriculum Learning.
+* Reset Parameters: 4, corresponding to the height of the possible walls.
 
 ## Reacher
 

docs/Limitations-and-Common-Issues.md

 
 ## Unity SDK
 ### Headless Mode
-Currently headless mode is disabled. We hope to address these in a future version of Unity.
+If you enable Headless mode, you will not be able to collect visual 
+observations from your agents.
-Currently the speed of the game physics can only be increased to 100x real-time. The Academy also moves in time with FixedUpdate() rather than Update(), so game behavior tied to frame updates may be out of sync. 
-
-### macOS Metal Support
-When running a Unity Environment on macOS using Metal rendering, the application can crash when the lock-screen is open. The solution is to set rendering to OpenGL. This can be done by navigating: `Edit -> Project Settings -> Player`. Clicking on `Other Settings`. Unchecking `Auto Graphics API for Mac`. Setting `OpenGL Core` to be above `Metal` in the priority list.
+Currently the speed of the game physics can only be increased to 100x 
+real-time. The Academy also moves in time with FixedUpdate() rather than 
+Update(), so game behavior tied to frame updates may be out of sync. 
+
+### Python version
+As of version 0.3, we no longer support Python 2. 
-If you directly import your Unity environment without building it in the editor, you might need to give it additional permissions to execute it. 
+If you directly import your Unity environment without building it in the 
+editor, you might need to give it additional permissions to execute it. 
 
 If you receive such a permission error on macOS, run:
 
 
 `chmod -R 755 *.x86_64` 
 
-On Windows, you can find instructions [here](https://technet.microsoft.com/en-us/library/cc754344(v=ws.11).aspx).
+On Windows, you can find instructions 
+[here](https://technet.microsoft.com/en-us/library/cc754344(v=ws.11).aspx).
-If you are able to launch the environment from `UnityEnvironment` but then receive a timeout error, there may be a number of possible causes.
- * _Cause_: There may be no Brains in your environment which are set to `External`.  In this case, the environment will not attempt to communicate with python. _Solution_: Set the train you wish to externally control through the Python API to `External` from the Unity Editor, and rebuild the environment.
- * _Cause_: On OSX, the firewall may be preventing communication with the environment. _Solution_: Add the built environment binary to the list of exceptions on the firewall by following instructions [here](https://support.apple.com/en-us/HT201642). 
-
-### Filename not found
-
-If you receive a file-not-found error while attempting to launch an environment, ensure that the environment files are in the root repository directory. For example, if there is a sub-folder containing the environment files, those files should be removed from the sub-folder and moved to the root. 
+If you are able to launch the environment from `UnityEnvironment` but 
+then receive a timeout error, there may be a number of possible causes.
+ * _Cause_: There may be no Brains in your environment which are set 
+ to `External`.  In this case, the environment will not attempt to 
+ communicate with python. _Solution_: Set the Brains(s) you wish to 
+ externally control through the Python API to `External` from the 
+ Unity Editor, and rebuild the environment.
+ * _Cause_: On OSX, the firewall may be preventing communication with 
+ the environment. _Solution_: Add the built environment binary to the 
+ list of exceptions on the firewall by following instructions 
+ [here](https://support.apple.com/en-us/HT201642). 
+ * _Cause_: An error happened in the Unity Environment preventing 
+ communication. _Solution_: Look into the 
+ [log files](https://docs.unity3d.com/Manual/LogFiles.html) 
+ generated by the Unity Environment to figure what error happened. 
-If you receive an exception `"Couldn't launch new environment because communication port {} is still in use. "`, you can change the worker number in the python script when calling 
+If you receive an exception `"Couldn't launch new environment because 
+communication port {} is still in use. "`, you can change the worker 
+number in the python script when calling 
-If you receive a message `Mean reward : nan` when attempting to train a model using PPO, this is due to the episodes of the learning environment not terminating. In order to address this, set `Max Steps` for either the Academy or Agents within the Scene Inspector to a value greater than 0. Alternatively, it is possible to manually set `done` conditions for episodes from within scripts for custom episode-terminating events.
+If you receive a message `Mean reward : nan` when attempting to train a 
+model using PPO, this is due to the episodes of the learning environment 
+not terminating. In order to address this, set `Max Steps` for either 
+the Academy or Agents within the Scene Inspector to a value greater 
+than 0. Alternatively, it is possible to manually set `done` conditions 
+for episodes from within scripts for custom episode-terminating events.

docs/ML-Agents-Overview.md

 
 The
 [Getting Started with the 3D Balance Ball Example](Getting-Started-with-Balance-Ball.md)
-tutorial covers this training mode with the **Balance Ball** sample environment.
+tutorial covers this training mode with the **3D Balance Ball** sample environment.
 
 ### Custom Training and Inference
 
 must react to events or games where agents can take actions of variable 
 duration. Switching between decision taking at every step and 
 on-demand-decision is one button click away. You can learn more about the 
-on-demand-decision feature [here](Feature-On-Demand-Decision.md).
+on-demand-decision feature [here](Feature-On-Demand-Decisions.md).
 
 * **Memory-enhanced Agents** - In some scenarios, agents must learn to 
 remember the past in order to take the 
 To help you use ML-Agents, we've created several in-depth tutorials 
 for [installing ML-Agents](Installation.md), 
 [getting started](Getting-Started-with-Balance-Ball.md) 
-with a sample Balance Ball environment (one of our many 
+with the 3D Balance Ball environment (one of our many 
 [sample environments](Learning-Environment-Examples.md)) and 
 [making your own environment](Learning-Environment-Create-New.md).
 

docs/Readme.md

  * [Installation & Set-up](Installation.md)
     * [Background: Jupyter Notebooks](Background-Jupyter.md)
     * [Docker Set-up (Experimental)](Using-Docker.md)
- * [Getting Started with the Balance Ball Environment](Getting-Started-with-Balance-Ball.md)
+ * [Getting Started with the 3D Balance Ball Environment](Getting-Started-with-Balance-Ball.md)
  * [Example Environments](Learning-Environment-Examples.md)
 
 ## Creating Learning Environments
 ## Help
  * [ML-Agents Glossary](Glossary.md)
  * [Limitations & Common Issues](Limitations-and-Common-Issues.md)
+ * [Migrating to ML-Agents v0.3](Migrating-v0.3.md)
  
 ## C# API and Components
  * Academy

docs/Using-Docker.md

 
 Run the Docker container by calling the following command at the top-level of the repository:
 ```
-docker run --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
-     <image-name>:latest <environment-name> \
-     --docker-target-name=unity-volume \
-     --train --run-id=<run-id>
+docker run --name <container-name> \
+           --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
+           <image-name>:latest <environment-name> \
+           --docker-target-name=unity-volume \
+           --train \
+           --run-id=<run-id>
+- `<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>` and `<environment-name>`: References the image and environment names, respectively.
 - `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. 
 For the `3DBall` environment, for example this would be:
 
 ```
-docker run --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
-     balance.ball.v0.1:latest 3Dball \
-     --docker-target-name=unity-volume \
-     --train --run-id=3dball_first_trial
+docker run --name 3DBallContainer.first.trial \
+           --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
+           balance.ball.v0.1:latest 3Dball \
+           --docker-target-name=unity-volume \
+           --train \
+           --run-id=3dball_first_trial
+
+
+### Stopping Container and Saving State
+
+If you are satisfied with the training progress, you can stop the Docker container while saving state using the following command:
+
+```
+docker kill --signal=SIGINT <container-name>
+```
+
+`<container-name>` is the name of the container specified in the earlier `docker run` command. If you didn't specify one, you can find the randomly generated identifier by running `docker container ls`.

python/Basics.ipynb

    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Unity ML Agents\n",
+    "# Unity ML-Agents\n",
-    "This notebook contains a walkthrough of the basic functions of the Python API for Unity ML Agents. For instructions on building a Unity environment, see [here](https://github.com/Unity-Technologies/ml-agents/wiki/Getting-Started-with-Balance-Ball)."
+    "This notebook contains a walkthrough of the basic functions of the Python API for Unity ML-Agents. For instructions on building a Unity environment, see [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Getting-Started-with-Balance-Ball.md)."
    ]
   },
   {
-    "### 1. Load dependencies"
+    "### 1. Set environment parameters\n",
+    "\n",
+    "Be sure to set `env_name` to the name of the Unity environment file you want to launch."
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "from unityagents import UnityEnvironment\n",
-    "\n",
-    "%matplotlib inline"
+    "env_name = \"3DBall\"  # Name of the Unity environment binary to launch\n",
+    "train_mode = True  # Whether to run the environment in training or inference mode"
    ]
   },
   {
-    "### 2. Set environment parameters\n",
-    "\n",
-    "Be sure to set `env_name` to the name of the Unity environment file you want to launch."
+    "### 2. Load dependencies"
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
-    "env_name = \"3DBall\" # Name of the Unity environment binary to launch\n",
-    "train_mode = True # Whether to run the environment in training or inference mode"
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from unityagents import UnityEnvironment\n",
+    "\n",
+    "%matplotlib inline"
    ]
   },
   {
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "env = UnityEnvironment(file_name=env_name)\n",
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Reset the environment\n",
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "for episode in range(10):\n",
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "env.close()"
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,

python/tests/test_bc.py

       "brainNames": ["RealFakeBrain"],
       "externalBrainNames": ["RealFakeBrain"],
       "logPath":"RealFakePath",
-      "apiNumber":"API-2",
+      "apiNumber":"API-3",
       "brainParameters": [{
           "vectorObservationSize": 3,
           "numStackedVectorObservations": 2,
       "brainNames": ["RealFakeBrain"],
       "externalBrainNames": ["RealFakeBrain"],
       "logPath":"RealFakePath",
-      "apiNumber":"API-2",
+      "apiNumber":"API-3",
       "brainParameters": [{
           "vectorObservationSize": 3,
           "numStackedVectorObservations": 2,

python/tests/test_ppo.py

       "brainNames": ["RealFakeBrain"],
       "externalBrainNames": ["RealFakeBrain"],
       "logPath":"RealFakePath",
-      "apiNumber":"API-2",
+      "apiNumber":"API-3",
       "brainParameters": [{
           "vectorObservationSize": 3,
           "numStackedVectorObservations": 2,
       "brainNames": ["RealFakeBrain"],
       "externalBrainNames": ["RealFakeBrain"],
       "logPath":"RealFakePath",
-      "apiNumber":"API-2",
+      "apiNumber":"API-3",
       "brainParameters": [{
           "vectorObservationSize": 3,
           "numStackedVectorObservations": 2,

python/tests/test_unityagents.py

   "brainNames": ["RealFakeBrain"],
   "externalBrainNames": ["RealFakeBrain"],
   "logPath":"RealFakePath",
-  "apiNumber":"API-2",
+  "apiNumber":"API-3",
   "brainParameters": [{
       "vectorObservationSize": 3,
       "numStackedVectorObservations": 2,

python/tests/test_unitytrainers.py

   "brainNames": ["RealFakeBrain"],
   "externalBrainNames": ["RealFakeBrain"],
   "logPath":"RealFakePath",
-  "apiNumber":"API-2",
+  "apiNumber":"API-3",
   "brainParameters": [{
       "vectorObservationSize": 3,
       "numStackedVectorObservations" : 2,

python/trainer_config.yaml

     summary_freq: 1000
     use_recurrent: false
 
+BananaBrain:
+    normalize: false
+    batch_size: 1024
+    beta: 5.0e-3
+    buffer_size: 10240
+
-    max_steps: 2.0e5
-    batch_size: 1048
-    buffer_size: 10240
+    max_steps: 5.0e4
+    batch_size: 128
+    buffer_size: 2048
-    hidden_units: 128
+    hidden_units: 256
+
+SmallWallBrain: 
+    max_steps: 2.0e5
+    batch_size: 128
+    buffer_size: 2048
+    beta: 5.0e-3
+    hidden_units: 256
+    summary_freq: 2000
+    time_horizon: 128
+    num_layers: 2
+    normalize: false
+
+BigWallBrain: 
+    max_steps: 2.0e5
+    batch_size: 128
+    buffer_size: 2048
+    beta: 5.0e-3
+    hidden_units: 256
+    summary_freq: 2000
+    time_horizon: 128
+    num_layers: 2
+    normalize: false
 
 StrikerBrain:
     max_steps: 1.0e5
     batch_size: 32
     buffer_size: 320
 
-HallwayBrainDC:
-    num_layers: 3
-    hidden_units: 256
-    beta: 1.0e-2
-    gamma: 0.99
-    num_epoch: 3
-    buffer_size: 512
-    batch_size: 64
-    max_steps: 5.0e5
-    summary_freq: 1000
-    time_horizon: 64
-
-BrainWallJumpDC:
+HallwayBrain:
+    memory_size: 256
-    buffer_size: 16
-    batch_size: 2
-    max_steps: 5.0e5
-    summary_freq: 1000
-    time_horizon: 64
-
-HallwayBrainDCLSTM:
-    use_recurrent: true
-    sequence_length: 64
-    num_layers: 2
-    hidden_units: 128
-    beta: 1.0e-2
-    gamma: 0.99
-    num_epoch: 3
-    buffer_size: 16
-    batch_size: 2
+    buffer_size: 1024
+    batch_size: 128
     max_steps: 5.0e5
     summary_freq: 1000
     time_horizon: 64

python/unityagents/environment.py

         atexit.register(self.close)
         self.port = base_port + worker_id
         self._buffer_size = 12000
-        self._python_api = "API-2"
+        self._version_ = "API-3"
         self._loaded = False
         self._open_socket = False
 
                     .format(str(file_name)))
 
             if "apiNumber" not in p:
-                self._unity_api = "API-1"
+                self._unity_version = "API-1"
-                self._unity_api = p["apiNumber"]
-            if self._unity_api != self._python_api:
+                self._unity_version = p["apiNumber"]
+            if self._unity_version != self._version_:
-                    "of ML-Agents.".format(self._python_api, self._unity_api))
+                    "of ML-Agents.".format(self._version_, self._unity_version))
             self._data = {}
             self._global_done = None
             self._academy_name = p["AcademyName"]

python/unitytrainers/models.py

         self.selected_actions = c_layers.one_hot_encoding(self.action_holder, self.a_size)
 
         self.all_old_probs = tf.placeholder(shape=[None, self.a_size], dtype=tf.float32, name='old_probabilities')
-        self.probs = tf.reduce_sum(self.all_probs * self.selected_actions, axis=1)
-        self.old_probs = tf.reduce_sum(self.all_old_probs * self.selected_actions, axis=1)
+
+        # We reshape these tensors to [batch x 1] in order to be of the same rank as continuous control probabilities.
+        self.probs = tf.expand_dims(tf.reduce_sum(self.all_probs * self.selected_actions, axis=1), 1)
+        self.old_probs = tf.expand_dims(tf.reduce_sum(self.all_old_probs * self.selected_actions, axis=1), 1)
 
     def create_cc_actor_critic(self, h_size, num_layers):
         num_streams = 2
         a = tf.exp(-1 * tf.pow(tf.stop_gradient(self.output) - self.mu, 2) / (2 * self.sigma_sq))
         b = 1 / tf.sqrt(2 * self.sigma_sq * np.pi)
         self.all_probs = tf.multiply(a, b, name="action_probs")
-        self.probs = tf.reduce_prod(self.all_probs, axis=1)
-        self.entropy = tf.reduce_sum(0.5 * tf.log(2 * np.pi * np.e * self.sigma_sq))
+        self.entropy = tf.reduce_mean(0.5 * tf.log(2 * np.pi * np.e * self.sigma_sq))
-        self.old_probs = tf.reduce_prod(self.all_old_probs, axis=1)
+        # We keep these tensors the same name, but use new nodes to keep code parallelism with discrete control.
+        self.probs = tf.identity(self.all_probs)
+        self.old_probs = tf.identity(self.all_old_probs)

python/unitytrainers/ppo/models.py

         """
 
         self.returns_holder = tf.placeholder(shape=[None], dtype=tf.float32, name='discounted_rewards')
-        self.advantage = tf.placeholder(shape=[None], dtype=tf.float32, name='advantages')
+        self.advantage = tf.placeholder(shape=[None, 1], dtype=tf.float32, name='advantages')
         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')
         v_opt_b = tf.squared_difference(self.returns_holder, clipped_value_estimate)
         self.value_loss = tf.reduce_mean(tf.boolean_mask(tf.maximum(v_opt_a, v_opt_b), self.mask))
 
+        # 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
+        # as an average of probabilities, or as a joint probability.
+
         self.loss = self.policy_loss + 0.5 * self.value_loss - decay_beta * tf.reduce_mean(
             tf.boolean_mask(entropy, self.mask))
         self.update_batch = optimizer.minimize(self.loss)

python/unitytrainers/ppo/trainer.py

         total_v, total_p = 0, 0
         advantages = self.training_buffer.update_buffer['advantages'].get_batch()
         self.training_buffer.update_buffer['advantages'].set(
-            (advantages - advantages.mean()) / advantages.std() + 1e-10)
+            (advantages - advantages.mean()) / (advantages.std() + 1e-10))
         for k in range(num_epoch):
             self.training_buffer.update_buffer.shuffle()
             for l in range(len(self.training_buffer.update_buffer['actions']) // n_sequences):
                              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]),
+                             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])}
                 if self.is_continuous:
                         _obs = np.array(_buffer['observations%d' % i][start:end])
                         (_batch, _seq, _w, _h, _c) = _obs.shape
                         feed_dict[self.model.visual_in[i]] = _obs.reshape([-1, _w, _h, _c])
-                # Memories are zeros
-                    # feed_dict[self.model.memory_in] = np.zeros([batch_size, self.m_size])
                     feed_dict[self.model.memory_in] = np.array(_buffer['memory'][start:end])[:, 0, :]
                 v_loss, p_loss, _ = self.sess.run(
                     [self.model.value_loss, self.model.policy_loss,

unity-environment/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DDecision.cs

-using System.Collections;
-using System.Collections.Generic;
+using System.Collections.Generic;
 using UnityEngine;
 
 public class Ball3DDecision : MonoBehaviour, Decision
-    public float[] Decide(List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
+    public float[] Decide(
+        List<float> vectorObs,
+        List<Texture2D> visualObs,
+        float reward,
+        bool done,
+        List<float> memory)
-        if (gameObject.GetComponent<Brain>().brainParameters.vectorActionSpaceType == SpaceType.continuous)
+        if (gameObject.GetComponent<Brain>().brainParameters.vectorActionSpaceType
+            == SpaceType.continuous)
         {
             List<float> act = new List<float>();
 
-            act.Add(state[5] * rotationSpeed);
+            act.Add(vectorObs[5] * rotationSpeed);
-            act.Add(-state[7] * rotationSpeed);
+            act.Add(-vectorObs[7] * rotationSpeed);
-        else
-        {
-            return new float[1]{ 1f };
-        }
+        return new float[1] { 1f };
-    public List<float> MakeMemory(List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
+    public List<float> MakeMemory(
+        List<float> vectorObs,
+        List<Texture2D> visualObs,
+        float reward,
+        bool done,
+        List<float> memory)
     {
         return new List<float>();
     }

unity-environment/Assets/ML-Agents/Examples/Banana/Prefabs/BANANA.prefab

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unity-environment/Assets/ML-Agents/Examples/Banana/Prefabs/StudentAgent.prefab

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-  value: 0
-  CumulativeReward: 0
-  stepCounter: 0
-  agentStoredAction: []
-  memory: []
-  id: 0
+  agentParameters:
+    agentCameras: []
+    maxStep: 0
+    resetOnDone: 1
+    onDemandDecision: 0
+    numberOfActionsBetweenDecisions: 1
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-  yForce: 300
-  zForce: 100
+  moveSpeed: 2
-  myLazer: {fileID: 1711242980656446}
+  myLaser: {fileID: 0}
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unity-environment/Assets/ML-Agents/Examples/Banana/Prefabs/TeacherAgent.prefab

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+  myLaser: {fileID: 0}
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unity-environment/Assets/ML-Agents/Examples/Banana/Prefabs/BADBANANA.prefab

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unity-environment/Assets/ML-Agents/Examples/Banana/Scripts/BananaAgent.cs

     float frozenTime;
     float effectTime;
     Rigidbody agentRB;
-    public float turnSpeed;
-    public float xForce;
-    public float yForce;
-    public float zForce;
+
+    // Speed of agent rotation.
+    public float turnSpeed = 300;
+
+    // Speed of agent movement.
+    public float moveSpeed = 2;
-    public GameObject myLazer;
+    public GameObject myLaser;
+    RayPerception rayPer;
-        agentRB = GetComponent<Rigidbody>(); // cache the RB
+        agentRB = GetComponent<Rigidbody>();
+        rayPer = GetComponent<RayPerception>();
         myAcademy = myAcademyObj.GetComponent<BananaAcademy>();
     }
 
         float[] rayAngles = { 20f, 90f, 160f, 45f, 135f, 70f, 110f };
         string[] detectableObjects = { "banana", "agent", "wall", "badBanana", "frozenAgent" };
-        RayPerception(rayDistance, rayAngles, detectableObjects);
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 0f, 0f));
         Vector3 localVelocity = transform.InverseTransformDirection(agentRB.velocity);
         AddVectorObs(localVelocity.x);
         AddVectorObs(localVelocity.z);
 
-
-    public void RayPerception(float rayDistance, float[] rayAngles, string[] detectableObjects) {
-        foreach (float angle in rayAngles)
-        {
-            float noise = 0f;
-            float noisyAngle = angle + Random.Range(-noise, noise);
-            Vector3 position = transform.TransformDirection(GiveCatersian(rayDistance, noisyAngle));
-            Debug.DrawRay(transform.position, position, Color.green, 0.0f, true);
-            RaycastHit hit;
-            float[] subList = new float[detectableObjects.Length + 2];
-            if (Physics.SphereCast(transform.position, 1.0f, position, out hit, rayDistance))
-            {
-                for (int i = 0; i < detectableObjects.Length; i++)
-                {
-                    if (hit.collider.gameObject.CompareTag(detectableObjects[i]))
-                    {
-                        subList[i] = 1;
-                        subList[detectableObjects.Length + 1] = hit.distance / rayDistance;
-                        break;
-                    }
-                }
-            }
-            else
-            {
-                subList[detectableObjects.Length] = 1f;
-            }
-            foreach (float f in subList)
-                AddVectorObs(f);
-        }
-    }
-
-    public Vector3 GiveCatersian(float radius, float angle)
-    {
-        float x = radius * Mathf.Cos(DegreeToRadian(angle));
-        float z = radius * Mathf.Sin(DegreeToRadian(angle));
-        return new Vector3(x, -0.1f, z);
-    }
-
-    public float DegreeToRadian(float degree)
-    {
-        return degree * Mathf.PI / 180f;
-    }
-
     public Color32 ToColor(int HexVal)
     {
         byte R = (byte)((HexVal >> 16) & 0xFF);
                 dirToGo *= 0.5f;
                 agentRB.velocity *= 0.75f;
             }
-            agentRB.AddForce(new Vector3(dirToGo.x * xForce, dirToGo.y * yForce, dirToGo.z * zForce), 
-                             ForceMode.Acceleration);
-            transform.Rotate(rotateDir, Time.deltaTime * turnSpeed);
+            agentRB.AddForce(dirToGo * moveSpeed, ForceMode.VelocityChange);
+            transform.Rotate(rotateDir, Time.fixedDeltaTime * turnSpeed);
         }
 
         if (agentRB.velocity.sqrMagnitude > 25f) // slow it down
 
         if (shoot)
         {
-            myLazer.transform.localScale = new Vector3(1f, 1f, 1f);
-            Vector3 position = transform.TransformDirection(GiveCatersian(25f, 90f));
+            myLaser.transform.localScale = new Vector3(1f, 1f, 1f);
+            Vector3 position = transform.TransformDirection(RayPerception.PolarToCartesian(25f, 90f));
             Debug.DrawRay(transform.position, position, Color.red, 0f, true);
             RaycastHit hit;
             if (Physics.SphereCast(transform.position, 2f, position, out hit, 25f))
         }
         else
         {
-            myLazer.transform.localScale = new Vector3(0f, 0f, 0f);
+            myLaser.transform.localScale = new Vector3(0f, 0f, 0f);
 
         }
 
         shoot = false;
         agentRB.velocity = Vector3.zero;
         bananas = 0;
-        myLazer.transform.localScale = new Vector3(0f, 0f, 0f);
+        myLaser.transform.localScale = new Vector3(0f, 0f, 0f);
         transform.position = new Vector3(Random.Range(-myArea.range, myArea.range), 
                                          2f, Random.Range(-myArea.range, myArea.range)) 
             + area.transform.position;

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

-using System.Collections;
-using System.Collections.Generic;
+using System.Collections.Generic;
-
-    public float[] Decide(List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
+    public float[] Decide(
+        List<float> vectorObs,
+        List<Texture2D> visualObs,
+        float reward,
+        bool done,
+        List<float> memory)
-        return new float[1]{ 1f };
-
+        return new float[1] { 1f };
-    public List<float> MakeMemory(List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
+    public List<float> MakeMemory(
+        List<float> vectorObs,
+        List<Texture2D> visualObs,
+        float reward,
+        bool done,
+        List<float> memory)
-
     }
 }

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

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unity-environment/Assets/ML-Agents/Examples/Hallway/Prefabs/HallwayArea.prefab

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unity-environment/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayAgent.cs

-// Put this script on your blue cube.
-
-using System.Collections;
+using System.Collections;
-    public GameObject ground; // ground game object. we will use the area bounds to spawn the blocks
+    public GameObject ground;
-
-    public GameObject goalA;
-    public GameObject goalB;
-    public GameObject orangeBlock; // the orange block we are going to be pushing
-    public GameObject violetBlock;
-    Rigidbody shortBlockRB;  // cached on initialization
-    Rigidbody agentRB;  // cached on initialization
-    Material groundMaterial; // cached on Awake()
+    public GameObject orangeGoal;
+    public GameObject redGoal;
+    public GameObject orangeBlock;
+    public GameObject redBlock;
+    RayPerception rayPer;
+    Rigidbody shortBlockRB;
+    Rigidbody agentRB;
+    Material groundMaterial;
-
     int selection;
 
     public override void InitializeAgent()
-        brain = FindObjectOfType<Brain>(); // only one brain in the scene so this should find our brain. BRAAAINS.
-
-        agentRB = GetComponent<Rigidbody>(); // cache the agent rigidbody
-        groundRenderer = ground.GetComponent<Renderer>(); // get the ground renderer so we can change the material when a goal is scored
-        groundMaterial = groundRenderer.material; // starting material
-
+        brain = FindObjectOfType<Brain>();
+        rayPer = GetComponent<RayPerception>();
+        agentRB = GetComponent<Rigidbody>();
+        groundRenderer = ground.GetComponent<Renderer>();
+        groundMaterial = groundRenderer.material;
-    public void RayPerception(float rayDistance,
-                                 float[] rayAngles, string[] detectableObjects, float height)
-    {
-        foreach (float angle in rayAngles)
-        {
-            float noise = 0f;
-            float noisyAngle = angle + Random.Range(-noise, noise);
-            Vector3 position = transform.TransformDirection(GiveCatersian(rayDistance, noisyAngle));
-            position.y = height;
-            Debug.DrawRay(transform.position, position, Color.red, 0.1f, true);
-            RaycastHit hit;
-            float[] subList = new float[detectableObjects.Length + 2];
-            if (Physics.SphereCast(transform.position, 1.0f, position, out hit, rayDistance))
-            {
-                for (int i = 0; i < detectableObjects.Length; i++)
-                {
-                    if (hit.collider.gameObject.CompareTag(detectableObjects[i]))
-                    {
-                        subList[i] = 1;
-                        subList[detectableObjects.Length + 1] = hit.distance / rayDistance;
-                        break;
-                    }
-                }
-            }
-            else
-            {
-                subList[detectableObjects.Length] = 1f;
-            }
-            foreach (float f in subList)
-                AddVectorObs(f);
-        }
-    }
-
-    public Vector3 GiveCatersian(float radius, float angle)
-    {
-        float x = radius * Mathf.Cos(DegreeToRadian(angle));
-        float z = radius * Mathf.Sin(DegreeToRadian(angle));
-        return new Vector3(x, 1f, z);
-    }
-
-    public float DegreeToRadian(float degree)
-    {
-        return degree * Mathf.PI / 180f;
-    }
-
-
-        float rayDistance = 8.5f;
-        float[] rayAngles = { 0f, 45f, 90f, 135f, 180f };
-        string[] detectableObjects = { "goal", "orangeBlock", "redBlock", "wall" };
-        RayPerception(rayDistance, rayAngles, detectableObjects, 0f);
+        float rayDistance = 12f;
+        float[] rayAngles = { 20f, 60f, 90f, 120f, 160f };
+        string[] detectableObjects = { "orangeGoal", "redGoal", "orangeBlock", "redBlock", "wall" };
+        AddVectorObs((float)GetStepCount() / (float)agentParameters.maxStep);
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 0f, 0f));
-    // swap ground material, wait time seconds, then swap back to the regular ground material.
-        yield return new WaitForSeconds(time); // wait for 2 sec
+        yield return new WaitForSeconds(time);
-
 
     public void MoveAgent(float[] act)
     {
 
-
-        // If we're using Continuous control you will need to change the Action
         if (brain.brainParameters.vectorActionSpaceType == SpaceType.continuous)
         {
             dirToGo = transform.forward * Mathf.Clamp(act[0], -1f, 1f);
         {
             int action = Mathf.FloorToInt(act[0]);
-            if (action == 0)
-            {
-                dirToGo = transform.forward * 1f;
-            }
-            else if (action == 1)
+            switch (action)
-                dirToGo = transform.forward * -1f;
-            }
-            else if (action == 2)
-            {
-                rotateDir = transform.up * 1f;
-            }
-            else if (action == 3)
-            {
-                rotateDir = transform.up * -1f;
+                case 0:
+                    dirToGo = transform.forward * 1f;
+                    break;
+                case 1:
+                    dirToGo = transform.forward * -1f;
+                    break;
+                case 2:
+                    rotateDir = transform.up * 1f;
+                    break;
+                case 3:
+                    rotateDir = transform.up * -1f;
+                    break;
-        transform.Rotate(rotateDir, Time.deltaTime * 100f);
-        agentRB.AddForce(dirToGo * academy.agentRunSpeed, ForceMode.VelocityChange); // GO
+        transform.Rotate(rotateDir, Time.deltaTime * 150f);
+        agentRB.AddForce(dirToGo * academy.agentRunSpeed, ForceMode.VelocityChange);
-        AddReward(-0.0003f);
-
-        MoveAgent(vectorAction); //perform agent actions
-        bool fail = false;  // did the agent or block get pushed off the edge?
-
-        if (!Physics.Raycast(agentRB.position, Vector3.down, 20)) // if the agent has gone over the edge, we done.
-        {
-            fail = true; // fell off bro
-            AddReward(-1f); // BAD AGENT
-                          //transform.position =  GetRandomSpawnPos(agentSpawnAreaBounds, agentSpawnArea);
-            Done(); // if we mark an agent as done it will be reset automatically. AgentReset() will be called.
-        }
-
-        if (fail)
-        {
-            StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, .5f)); // swap ground material to indicate fail
-        }
+        AddReward(-1f / agentParameters.maxStep);
+        MoveAgent(vectorAction);
-    // detect when we touch the goal
-        if (col.gameObject.CompareTag("goal")) // touched goal
+        if (col.gameObject.CompareTag("orangeGoal") || col.gameObject.CompareTag("redGoal"))
-            if ((selection == 0 && col.gameObject.name == "GoalA") || (selection == 1 && col.gameObject.name == "GoalB"))
+            if ((selection == 0 && col.gameObject.CompareTag("orangeGoal")) ||
+                (selection == 1 && col.gameObject.CompareTag("redGoal")))
-                AddReward(1f); // you get 5 points
-                StartCoroutine(GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 2)); // swap ground material for a bit to indicate we scored.
+                SetReward(1f);
+                StartCoroutine(GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 0.5f));
-                AddReward(-0.1f); // you lose a point
-                StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, .5f)); // swap ground material to indicate fail
+                SetReward(-0.1f);
+                StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, 0.5f));
-            Done(); // if we mark an agent as done it will be reset automatically. AgentReset() will be called.
+            Done();
-    // In the editor, if "Reset On Done" is checked then AgentReset() will be called automatically anytime we mark done = true in an agent script.
+        float agentOffset = -15f;
+        float blockOffset = 0f;
-            orangeBlock.transform.position = new Vector3(0f + Random.Range(-3f, 3f), 2f, -15f + Random.Range(-5f, 5f)) + ground.transform.position;
-            violetBlock.transform.position = new Vector3(0f, -1000f, -15f + Random.Range(-5f, 5f)) + ground.transform.position;
+            orangeBlock.transform.position =
+                new Vector3(0f + Random.Range(-3f, 3f), 2f, blockOffset + Random.Range(-5f, 5f))
+                + ground.transform.position;
+            redBlock.transform.position =
+                new Vector3(0f, -1000f, blockOffset + Random.Range(-5f, 5f))
+                + ground.transform.position;
-            orangeBlock.transform.position = new Vector3(0f, -1000f, -15f + Random.Range(-5f, 5f)) + ground.transform.position;
-            violetBlock.transform.position = new Vector3(0f, 2f, -15f + Random.Range(-5f, 5f)) + ground.transform.position;
+            orangeBlock.transform.position =
+                           new Vector3(0f, -1000f, blockOffset + Random.Range(-5f, 5f))
+                           + ground.transform.position;
+            redBlock.transform.position =
+                new Vector3(0f, 2f, blockOffset + Random.Range(-5f, 5f))
+                + ground.transform.position;
-        transform.position = new Vector3(0f+ Random.Range(-3f, 3f), 1f, 0f + Random.Range(-5f, 5f)) + ground.transform.position;
+
+        transform.position = new Vector3(0f + Random.Range(-3f, 3f),
+                                         1f, agentOffset + Random.Range(-5f, 5f))
+            + ground.transform.position;
+
+        int goalPos = Random.Range(0, 2);
+        if (goalPos == 0)
+        {
+            orangeGoal.transform.position = new Vector3(7f, 0.5f, 9f) + area.transform.position;
+            redGoal.transform.position = new Vector3(-7f, 0.5f, 9f) + area.transform.position;
+        }
+        else
+        {
+            redGoal.transform.position = new Vector3(7f, 0.5f, 9f) + area.transform.position;
+            orangeGoal.transform.position = new Vector3(-7f, 0.5f, 9f) + area.transform.position;
+        }
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unity-environment/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs

     Rigidbody blockRB;  //cached on initialization
     Rigidbody agentRB;  //cached on initialization
     Material groundMaterial; //cached on Awake()
+    RayPerception rayPer;
 
     /// <summary>
     /// We will be changing the ground material based on success/failue
         base.InitializeAgent();
         goalDetect = block.GetComponent<GoalDetect>();
         goalDetect.agent = this;
+        rayPer = GetComponent<RayPerception>();
 
         // Cache the agent rigidbody
         agentRB = GetComponent<Rigidbody>();
 
     public override void CollectObservations()
     {
-        // Block position relative to goal.
-        Vector3 blockPosRelToGoal = blockRB.position - goal.transform.position;
-        // Block position relative to agent.
-        Vector3 blockPosRelToAgent = blockRB.position - agentRB.position;
-        // Obstacle position relative to agent.
-
-        // Agent position relative to ground.
-        Vector3 agentPos = agentRB.position - area.transform.position;
-        // Goal position relative to ground.
-        Vector3 goalPos = goal.transform.position - ground.transform.position;
-
-        AddVectorObs(agentPos);
-        AddVectorObs(goalPos);
-        AddVectorObs(blockPosRelToGoal);
-        AddVectorObs(blockPosRelToAgent);
-
-        // Add velocity of block and agent to observations.
-        AddVectorObs(blockRB.velocity);
-        AddVectorObs(agentRB.velocity);
+        float rayDistance = 12f;
+        float[] rayAngles = { 0f, 45f, 90f, 135f, 180f, 110f, 70f };
+        string[] detectableObjects;
+        detectableObjects = new string[] { "block", "goal", "wall" };
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 0f, 0f));
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 1.5f, 0f));
-    public Vector3 GetRandomSpawnPos(float spawnHeight)
+    public Vector3 GetRandomSpawnPos()
     {
         bool foundNewSpawnLocation = false;
         Vector3 randomSpawnPos = Vector3.zero;
         Done();
 
         // Swap ground material for a bit to indicate we scored.
-        StartCoroutine(GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 1));
+        StartCoroutine(GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 0.5f));
     }
 
     /// <summary>
     /// </summary>
 	public void MoveAgent(float[] act)
     {
-        // AGENT ACTIONS
-        // Here we define the actions our agent can use, such as
-        // "go left", "go forward", "turn", etc.
-        // In the brain we  define the number of axes we want to use here. 
-        // In this example we need 2 axes to define:
-        // Right/left movement (act[0])
-        // Forward/back movement (act[1])
+        Vector3 dirToGo = Vector3.zero;
+        Vector3 rotateDir = Vector3.zero;
-        // Example: Right/Left Movement. It is defined in this line:
-        // Vector3 directionX = Vector3.right * Mathf.Clamp(act[0], -1f, 1f);
-
-        // The neural network is setting the act[0] value.
-        // If it chooses 1 then the agent will go right. 
-        // If it chooses -1 the agent will go left. 
-        // If it chooses .42 then it will go a little bit right
-        // If it chooses -.8 then it will go left (well...80% left)
-
-        // Energy Conservation Penalties
-        // Give penalties based on how fast the agent chooses to go. 
-        // The agent should only exert as much energy as necessary.
-        // This is how animals work as well. 
-        // i.e. You're probably not running in place at all times.
-
-        // Larger the value, the less the penalty is.
-        float energyConservPenaltyModifier = 10000;
-
-        // The larger the movement, the greater the penalty given.
-        AddReward(-Mathf.Abs(act[0]) / energyConservPenaltyModifier);
-        AddReward(-Mathf.Abs(act[1]) / energyConservPenaltyModifier);
-
-        Vector3 directionX = Vector3.zero;
-        Vector3 directionZ = Vector3.zero;
-
-
-        // Move left or right in world space.
-        directionX = Vector3.right * Mathf.Clamp(act[0], -1f, 1f);
-
-        // Move forward or back in world space.
-        directionZ = Vector3.forward * Mathf.Clamp(act[1], -1f, 1f);
-
-        // Add directions together. This is the direction we want the agent
-        // to move in.
-        Vector3 dirToGo = directionX + directionZ;
+        int action = Mathf.FloorToInt(act[0]);
-        // Apply movement force!
-        agentRB.AddForce(dirToGo * academy.agentRunSpeed, ForceMode.VelocityChange);
-        if (dirToGo != Vector3.zero)
+        // Goalies and Strikers have slightly different action spaces.
+        switch (action)
-            // Rotate the agent appropriately.
-            agentRB.rotation = Quaternion.Lerp(agentRB.rotation,
-                                               Quaternion.LookRotation(dirToGo),
-                                               Time.deltaTime * academy.agentRotationSpeed);
+            case 0:
+                dirToGo = transform.forward * 1f;
+                break;
+            case 1:
+                dirToGo = transform.forward * -1f;
+                break;
+            case 2:
+                rotateDir = transform.up * 1f;
+                break;
+            case 3:
+                rotateDir = transform.up * -1f;
+                break;
+            case 4:
+                dirToGo = transform.right * -0.75f;
+                break;
+            case 5:
+                dirToGo = transform.right * 0.75f;
+                break;
+        transform.Rotate(rotateDir, Time.fixedDeltaTime * 200f);
+        agentRB.AddForce(dirToGo * academy.agentRunSpeed,
+                         ForceMode.VelocityChange);
 
     }
 
         MoveAgent(vectorAction);
 
         // Penalty given each step to encourage agent to finish task quickly.
-        AddReward(-.00005f);
-
-        // Did the agent or block get pushed off the edge?
-        bool fail = false;
-
-        // If the agent has gone over the edge, end the episode.
-        if (!Physics.Raycast(agentRB.position, Vector3.down, 3))
-        {
-            // Fell off bro
-            fail = true;
-
-            // BAD AGENT
-            SetReward(-1f);
-
-            // If we mark an agent as done it will be reset automatically. 
-            // AgentReset() will be called.
-            Done();
-        }
-
-        // If the block has gone over the edge, end the episode.
-        if (!Physics.Raycast(blockRB.position, Vector3.down, 3))
-        {
-            // Fell off bro
-            fail = true;
-
-            // BAD AGENT
-            SetReward(-1f);
-
-            // If we mark an agent as done it will be reset automatically. 
-            // AgentReset() will be called.
-            Done();
-        }
-
-        if (fail)
-        {
-            // Swap ground material to indicate failure of the episode.
-            StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, 1f));
-        }
+        AddReward(-1f / agentParameters.maxStep);
     }
 
     /// <summary>
     {
         // Get a random position for the block.
-        block.transform.position = GetRandomSpawnPos(1.5f);
+        block.transform.position = GetRandomSpawnPos();
 
         // Reset block velocity back to zero.
         blockRB.velocity = Vector3.zero;
         area.transform.Rotate(new Vector3(0f, rotationAngle, 0f));
 
         ResetBlock();
-        transform.position = GetRandomSpawnPos(1.5f);
+        transform.position = GetRandomSpawnPos();
-

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   m_ObjectHideFlags: 0

unity-environment/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs

     [HideInInspector]
     public Rigidbody agentRB;
     SoccerAcademy academy;
-    Renderer renderer;
+    Renderer agentRenderer;
+    RayPerception rayPer;
-        renderer.material = team == Team.red ? academy.redMaterial : academy.blueMaterial;
+        agentRenderer.material = team == Team.red ? academy.redMaterial : academy.blueMaterial;
     }
 
     public void JoinRedTeam(AgentRole role)
-        renderer.material = academy.redMaterial;
+        agentRenderer.material = academy.redMaterial;
     }
 
     public void JoinBlueTeam(AgentRole role)
-        renderer.material = academy.blueMaterial;
+        agentRenderer.material = academy.blueMaterial;
-    void Awake()
+    public override void InitializeAgent()
-        renderer = GetComponent<Renderer>();
+        base.InitializeAgent();
+        agentRenderer = GetComponent<Renderer>();
+        rayPer = GetComponent<RayPerception>();
         academy = FindObjectOfType<SoccerAcademy>();
         PlayerState playerState = new PlayerState();
         playerState.agentRB = GetComponent<Rigidbody>();
         playerState.playerIndex = playerIndex;
     }
 
-    public override void InitializeAgent()
-    {
-        base.InitializeAgent();
-    }
-
-    public void RayPerception(float rayDistance,
-                             float[] rayAngles, string[] detectableObjects,
-                              float startHeight, float endHeight)
-    {
-        foreach (float angle in rayAngles)
-        {
-            float noise = 0f;
-            float noisyAngle = angle + Random.Range(-noise, noise);
-            Vector3 position = transform.TransformDirection(
-                GiveCatersian(rayDistance, noisyAngle));
-            position.y = endHeight;
-            Debug.DrawRay(transform.position + new Vector3(0f, startHeight, 0f),
-                          position, Color.red, 0.1f, true);
-            RaycastHit hit;
-            float[] subList = new float[detectableObjects.Length + 2];
-            if (Physics.SphereCast(transform.position +
-                                   new Vector3(0f, startHeight, 0f), 1.0f,
-                                   position, out hit, rayDistance))
-            {
-                for (int i = 0; i < detectableObjects.Length; i++)
-                {
-                    if (hit.collider.gameObject.CompareTag(detectableObjects[i]))
-                    {
-                        subList[i] = 1;
-                        subList[detectableObjects.Length + 1] = hit.distance / rayDistance;
-                        break;
-                    }
-                }
-            }
-            else
-            {
-                subList[detectableObjects.Length] = 1f;
-            }
-            foreach (float f in subList)
-                AddVectorObs(f);
-        }
-    }
-
-    public Vector3 GiveCatersian(float radius, float angle)
-    {
-        float x = radius * Mathf.Cos(DegreeToRadian(angle));
-        float z = radius * Mathf.Sin(DegreeToRadian(angle));
-        return new Vector3(x, 1f, z);
-    }
-
-    public float DegreeToRadian(float degree)
-    {
-        return degree * Mathf.PI / 180f;
-    }
-
     public override void CollectObservations()
     {
         float rayDistance = 20f;
             detectableObjects = new string[] { "ball", "blueGoal", "redGoal",
                 "wall", "blueAgent", "redAgent" };
         }
-        RayPerception(rayDistance, rayAngles, detectableObjects, 0f, 0f);
-        RayPerception(rayDistance, rayAngles, detectableObjects, 1f, 1f);
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 0f, 0f));
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 1f, 0f));
     }
 
     public void MoveAgent(float[] act)
         if (agentRole == AgentRole.goalie)
         {
             kickPower = 0f;
-            if (action == 0)
+            switch (action)
-                dirToGo = transform.forward * 1f;
-                kickPower = 1f;
+                case 0:
+                    dirToGo = transform.forward * 1f;
+                    kickPower = 1f;
+                    break;
+                case 1:
+                    dirToGo = transform.forward * -1f;
+                    break;
+                case 3:
+                    dirToGo = transform.right * -1f;
+                    break;
+                case 2:
+                    dirToGo = transform.right * 1f;
+                    break;
-            else if (action == 1)
-            {
-                dirToGo = transform.forward * -1f;
-            }
-            else if (action == 3)
-            {
-                dirToGo = transform.right * -1f;
-            }
-            else if (action == 2)
-            {
-                dirToGo = transform.right * 1f;
-            }
-
-            if (action == 0)
+            switch (action)
-                dirToGo = transform.forward * 1f;
-                kickPower = 1f;
-            }
-            else if (action == 1)
-            {
-                dirToGo = transform.forward * -1f;
-            }
-            else if (action == 2)
-            {
-                rotateDir = transform.up * 1f;
-            }
-            else if (action == 3)
-            {
-                rotateDir = transform.up * -1f;
-            }
-            else if (action == 4)
-            {
-                dirToGo = transform.right * -1f;
-            }
-            else if (action == 5)
-            {
-                dirToGo = transform.right * 1f;
+                case 0:
+                    dirToGo = transform.forward * 1f;
+                    kickPower = 1f;
+                    break;
+                case 1:
+                    dirToGo = transform.forward * -1f;
+                    break;
+                case 2:
+                    rotateDir = transform.up * 1f;
+                    break;
+                case 3:
+                    rotateDir = transform.up * -1f;
+                    break;
+                case 4:
+                    dirToGo = transform.right * -0.75f;
+                    break;
+                case 5:
+                    dirToGo = transform.right * 0.75f;
+                    break;
             }
         }
         transform.Rotate(rotateDir, Time.deltaTime * 100f);

unity-environment/Assets/ML-Agents/Examples/Template/Scripts/TemplateDecision.cs

-using System.Collections;
-using System.Collections.Generic;
+using System.Collections.Generic;
-    public float[] Decide(List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
+    public float[] Decide(
+        List<float> vectorObs,
+        List<Texture2D> visualObs,
+        float reward,
+        bool done,
+        List<float> memory)
-
-    public List<float> MakeMemory(List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
+    public List<float> MakeMemory(
+        List<float> vectorObs,
+        List<Texture2D> visualObs,
+        float reward,
+        bool done,
+        List<float> memory)
-        
     }
 }

unity-environment/Assets/ML-Agents/Examples/SharedAssets/Scripts/RandomDecision.cs

-using System.Collections;
-using System.Collections.Generic;
+using System.Collections.Generic;
 using UnityEngine;
 
 public class RandomDecision : MonoBehaviour, Decision
 
     public void Awake()
     {
-        brainParameters = 
+        brainParameters =
-    public float[] Decide(List<float> state, List<Texture2D> observation, 
-                          float reward, bool done, List<float> memory)
+    public float[] Decide(
+        List<float> vectorObs,
+        List<Texture2D> visualObs,
+        float reward,
+        bool done,
+        List<float> memory)
-        
         if (actionSpaceType == SpaceType.continuous)
         {
             List<float> act = new List<float>();
-                act.Add(2*Random.value-1);
+                act.Add(2 * Random.value - 1);
-        else
-        {
-            return new float[1] { Random.Range(0,actionSpaceSize)  };
-        }
+
+        return new float[1] { Random.Range(0, actionSpaceSize) };
-    public List<float> MakeMemory(List<float> state,
-                                  List<Texture2D> observation, float reward,
-                                  bool done, List<float> memory)
+    public List<float> MakeMemory(
+        List<float> vectorObs,
+        List<Texture2D> visualObs,
+        float reward,
+        bool done,
+        List<float> memory)
     {
         return new List<float>();
     }

unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/SuccessGround.mat

   m_ObjectHideFlags: 0
   m_PrefabParentObject: {fileID: 0}
   m_PrefabInternal: {fileID: 0}
-  m_Name: success ground green
+  m_Name: SuccessGround
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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/Obstacle.mat

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   m_PrefabInternal: {fileID: 0}
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+  m_Name: Obstacle
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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/Goal.mat

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-  m_Name: goal
+  m_Name: Goal
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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/FailGround.mat

   m_ObjectHideFlags: 0
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   m_PrefabInternal: {fileID: 0}
-  m_Name: fail ground red
+  m_Name: FailGround
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   m_LightmapFlags: 1

unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/Block.mat

   m_ObjectHideFlags: 0
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   m_PrefabInternal: {fileID: 0}
-  m_Name: block
+  m_Name: Block
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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/Ball.mat

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-  m_Name: ball
+  m_Name: Ball
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   m_LightmapFlags: 1

unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/MaterialsGrid/pitMaterial.mat

   m_ObjectHideFlags: 0
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+  m_Name: pitMaterial
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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/MaterialsGrid/goalMaterial.mat

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+  m_Name: goalMaterial
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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/MaterialsGrid/agentMaterial.mat

   m_ObjectHideFlags: 0
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   m_PrefabInternal: {fileID: 0}
-  m_Name: agent_mat
+  m_Name: agentMaterial
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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/MaterialsBall/Materials/logo2.mat

   m_ObjectHideFlags: 0
   m_PrefabParentObject: {fileID: 0}
   m_PrefabInternal: {fileID: 0}
-  m_Name: logo 2
+  m_Name: logo2
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   m_ShaderKeywords: 
   m_LightmapFlags: 4

unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/MaterialsBall/Materials/logo1.mat

   m_ObjectHideFlags: 0
   m_PrefabParentObject: {fileID: 0}
   m_PrefabInternal: {fileID: 0}
-  m_Name: logo 1
+  m_Name: logo1
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   m_ShaderKeywords: _ALPHAPREMULTIPLY_ON
   m_LightmapFlags: 4

unity-environment/Assets/ML-Agents/Scripts/Brain.cs

      * the state
      * <br> If discrete : The number of possible values the state can take*/
 
-    [Range(1, 10)]
+    [Range(1, 50)]
     public int numStackedVectorObservations = 1;
 
     public int vectorActionSize = 1;

unity-environment/Assets/ML-Agents/Scripts/Decision.cs

-using System.Collections;
-using System.Collections.Generic;
+using System.Collections.Generic;
-/// Generic functions for Decision Interface
+/// <summary>
+/// Interface for implementing the behavior of an Agent that uses a Heuristic
+/// Brain. The behavior of an Agent in this case is fully decided using the
+/// implementation of these methods and no training or inference takes place.
+/// Currently, the Heuristic Brain does not support text observations and actions.
+/// </summary>
-    /// \brief Implement this method to define the logic of decision making 
-    /// for the CoreBrainHeuristic
-    /** Given the information about the agent, return a vector of actions.
-    * @param state The state of the agent
-    * @param observation The cameras the agent uses
-    * @param reward The reward the agent had at the previous step
-    * @param done Whether or not the agent is done
-    * @param memory The memories stored from the previous step with MakeMemory()
-    * @return The vector of actions the agent will take at the next step
-    */
-    float[] Decide(List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory);
+    /// <summary>
+    /// Defines the decision-making logic of the agent. Given the information 
+    /// about the agent, returns a vector of actions.
+    /// </summary>
+    /// <returns>Vector action vector.</returns>
+    /// <param name="vectorObs">The vector observations of the agent.</param>
+    /// <param name="visualObs">The cameras the agent uses for visual observations.</param>
+    /// <param name="reward">The reward the agent received at the previous step.</param>
+    /// <param name="done">Whether or not the agent is done.</param>
+    /// <param name="memory">
+    /// The memories stored from the previous step with 
+    /// <see cref="MakeMemory(List{float}, List{Texture2D}, float, bool, List{float})"/>
+    /// </param>
+    float[] Decide(
+        List<float>
+        vectorObs,
+        List<Texture2D> visualObs,
+        float reward,
+        bool done,
+        List<float> memory);
-    /// \brief Implement this method to define the logic of memory making for 
-    /// the CoreBrainHeuristic
-    /** Given the information about the agent, return the new memory vector for the agent.
-    * @param state The state of the agent
-    * @param observation The cameras the agent uses
-    * @param reward The reward the agent had at the previous step
-    * @param done Weather or not the agent is done
-    * @param memory The memories stored from the previous step with MakeMemory()
-    * @return The vector of memories the agent will use at the next step
-    */
-    List<float> MakeMemory(List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory);
-}
+    /// <summary>
+    /// Defines the logic for creating the memory vector for the Agent.
+    /// </summary>
+    /// <returns>The vector of memories the agent will use at the next step.</returns>
+    /// <param name="vectorObs">The vector observations of the agent.</param>
+    /// <param name="visualObs">The cameras the agent uses for visual observations.</param>
+    /// <param name="reward">The reward the agent received at the previous step.</param>
+    /// <param name="done">Whether or not the agent is done.</param>
+    /// <param name="memory">
+    /// The memories stored from the previous call to this method.
+    /// </param>
+    List<float> MakeMemory(
+        List<float> vectorObs,
+        List<Texture2D> visualObs,
+        float reward,
+        bool done,
+        List<float> memory);
+}

unity-environment/Assets/ML-Agents/Scripts/ExternalCommunicator.cs

     StreamWriter logWriter;
     string logPath;
 
-    const string api = "API-2";
+    const string _version_ = "API-3";
 
     /// Placeholder for state information to send.
     [System.Serializable]
         accParamerters.brainParameters = new List<BrainParameters>();
         accParamerters.brainNames = new List<string>();
         accParamerters.externalBrainNames = new List<string>();
-        accParamerters.apiNumber = api;
+        accParamerters.apiNumber = _version_;
         accParamerters.logPath = logPath;
         foreach (Brain b in brains)
         {

unity-environment/Assets/ML-Agents/Scripts/Monitor.cs

-using System.Collections;
-using System.Collections.Generic;
-using UnityEngine;
-using UnityEngine.UI;
-using Newtonsoft.Json;
+using System.Collections.Generic;
+using UnityEngine;
-/** The type of monitor the information must be displayed in.
- * <slider> corresponds to a slingle rectangle which width is given
- * by a float between -1 and 1. (green is positive, red is negative)
- * <hist> corresponds to n vertical sliders. 
- * <text> is a text field.
- * <bar> is a rectangle of fixed length to represent the proportions 
- * of a list of floats.
- */ 
+using Newtonsoft.Json;
+
+/// <summary>
+/// The type of monitor the information must be displayed in.
+/// <slider> corresponds to a single rectangle whose width is given
+///     by a float between -1 and 1. (green is positive, red is negative)
+/// <hist> corresponds to n vertical sliders.
+/// <text> is a text field.
+/// <bar> is a rectangle of fixed length to represent the proportions
+///     of a list of floats.
+/// </summary>
 public enum MonitorType
 {
     slider,
 }
 
-/** Monitor is used to display information. Use the log function to add
- * information to your monitor.
- */ 
+/// <summary>
+/// Monitor is used to display information about the Agent within the Unity
+/// scene. Use the log function to add information to your monitor.
+/// </summary>
+    /// <summary>
+    /// Represents how high above the target the monitors will be.
+    /// </summary>
+    [HideInInspector]
+    static public float verticalOffset = 3f;
-    static bool isInstanciated;
+    static bool isInstantiated;
+    static Dictionary<Transform, Dictionary<string, DisplayValue>> displayTransformValues;
+    static Color[] barColors;
-    private struct DisplayValue
+    struct DisplayValue
-
-    static Dictionary<Transform, Dictionary<string,  DisplayValue>> displayTransformValues;
-    static private Color[] barColors;
-    [HideInInspector]
-    static public float verticalOffset = 3f;
-    /**< \brief This float represents how high above the target the monitors will be. */
 
     static GUIStyle keyStyle;
     static GUIStyle valueStyle;
     static bool initialized;
 
-
-    /** Use the Monitor.Log static function to attach information to a transform.
-     * If displayType is <text>, value can be any object. 
-     * If sidplayType is <slider>, value must be a float.
-     * If sidplayType is <hist>, value must be a List or Array of floats.
-     * If sidplayType is <bar>, value must be a list or Array of positive floats.
-     * Note that <slider> and <hist> caps values between -1 and 1.
-     * @param key The name of the information you wish to Log.
-     * @param value The value you want to display.
-     * @param displayType The type of display.
-     * @param target The transform you want to attach the information to.
-     */
+    /// <summary>
+    /// Use the Monitor.Log static function to attach information to a transform.
+    /// If displayType is <text>, value can be any object. 
+    /// If sidplayType is <slider>, value must be a float.
+    /// If sidplayType is <hist>, value must be a List or Array of floats.
+    /// If sidplayType is <bar>, value must be a list or Array of positive floats.
+    /// Note that <slider> and <hist> caps values between -1 and 1.
+    /// </summary>
+    /// <returns>The log.</returns>
+    /// <param name="key">The name of the information you wish to Log.</param>
+    /// <param name="value">The value you want to display.</param>
+    /// <param name="displayType">The type of display.</param>
+    /// <param name="target">
+    /// The transform you want to attach the information to.
+    /// </param>
     public static void Log(
         string key,
         object value,
-        if (!isInstanciated)
+        if (!isInstantiated)
-            InstanciateCanvas();
-            isInstanciated = true;
+            InstantiateCanvas();
+            isInstantiated = true;
 
         }
 
         }
     }
 
-    /** Remove a value from a monitor
-     * @param target The transform to which the information is attached
-     * @param key The key of the information you want to remove
-     */ 
+    /// <summary>
+    /// Remove a value from a monitor.
+    /// </summary>
+    /// <param name="target">
+    /// The transform to which the information is attached.
+    /// </param>
+    /// <param name="key">The key of the information you want to remove.</param>
     public static void RemoveValue(Transform target, string key)
     {
         if (target == null)
 
     }
 
-    /** Remove all information from a monitor
-     * @param target The transform to which the information is attached
-     */ 
+    /// <summary>
+    /// Remove all information from a monitor.
+    /// </summary>
+    /// <param name="target">
+    /// The transform to which the information is attached.
+    /// </param>
     public static void RemoveAllValues(Transform target)
     {
         if (target == null)
         {
             displayTransformValues.Remove(target);
         }
-    
+
-    /** Use SetActive to enable or disable the Monitor via script
-     * @param active Set the Monitor's status to the value of active
-     */ 
-    public static void SetActive(bool active){
-        if (!isInstanciated)
+    /// <summary>
+    /// Use SetActive to enable or disable the Monitor via script
+    /// </summary>
+    /// <param name="active">Value to set the Monitor's status to.</param>
+    public static void SetActive(bool active)
+    {
+        if (!isInstantiated)
-            InstanciateCanvas();
-            isInstanciated = true;
+            InstantiateCanvas();
+            isInstantiated = true;
 
         }
         if (canvas != null)
 
     }
 
-    private static void InstanciateCanvas()
+    /// Initializes the canvas.
+    static void InstantiateCanvas()
     {
         canvas = GameObject.Find("AgentMonitorCanvas");
         if (canvas == null)
             canvas.AddComponent<Monitor>();
         }
-        displayTransformValues = new Dictionary<Transform, Dictionary< string , DisplayValue>>();
+        displayTransformValues = new Dictionary<Transform, Dictionary<string, DisplayValue>>();
+    /// Convert the input object to a float array. Returns a float[0] if the
+    /// conversion process fails.
     float[] ToFloatArray(object input)
     {
         try
         }
         catch
         {
-            
+
         }
         try
         {
         }
         catch
         {
-            
+
+    /// <summary> <inheritdoc/> </summary>
     void OnGUI()
     {
         if (!initialized)
                 displayTransformValues.Remove(target);
                 continue;
             }
- 
+
             float widthScaler = (Screen.width / 1000f);
             float keyPixelWidth = 100 * widthScaler;
             float keyPixelHeight = 20 * widthScaler;
             {
                 continue;
             }
-                
+
 
             Dictionary<string, DisplayValue> displayValues = displayTransformValues[target];
 
                     valueStyle.alignment = TextAnchor.MiddleLeft;
                     GUI.Label(new Rect(
                             origin.x + paddingwidth + keyPixelWidth,
-                            origin.y - (index + 1) * keyPixelHeight, 
-                            keyPixelWidth, keyPixelHeight), 
+                            origin.y - (index + 1) * keyPixelHeight,
+                            keyPixelWidth, keyPixelHeight),
                         JsonConvert.SerializeObject(displayValues[key].value, Formatting.None), valueStyle);
 
                 }
-                    if (displayValues[key].value.GetType() == typeof(float))
+                    if (displayValues[key].value is float)
                     {
                         sliderValue = (float)displayValues[key].value;
                     }
                     }
                     GUI.Box(new Rect(
                             origin.x + paddingwidth + keyPixelWidth,
-                            origin.y - (index + 0.9f) * keyPixelHeight, 
-                            keyPixelWidth * sliderValue, keyPixelHeight * 0.8f), 
+                            origin.y - (index + 0.9f) * keyPixelHeight,
+                            keyPixelWidth * sliderValue, keyPixelHeight * 0.8f),
                         GUIContent.none, s);
 
                 }
                         }
                         GUI.Box(new Rect(
                                 origin.x + paddingwidth + keyPixelWidth + (keyPixelWidth * histWidth + paddingwidth / 2) * i,
-                                origin.y - (index + 0.1f) * keyPixelHeight, 
-                                keyPixelWidth * histWidth, -keyPixelHeight * value), 
+                                origin.y - (index + 0.1f) * keyPixelHeight,
+                                keyPixelWidth * histWidth, -keyPixelHeight * value),
                             GUIContent.none, s);
                     }
 
                 {
                     float[] vals = ToFloatArray(displayValues[key].value);
                     float valsSum = 0f;
-                    float valsCum = 0f; 
+                    float valsCum = 0f;
                     foreach (float f in vals)
                     {
                         valsSum += Mathf.Max(f, 0);
                             float value = Mathf.Max(vals[i], 0) / valsSum;
                             GUI.Box(new Rect(
                                     origin.x + paddingwidth + keyPixelWidth + keyPixelWidth * valsCum,
-                                    origin.y - (index + 0.9f) * keyPixelHeight, 
-                                    keyPixelWidth * value, keyPixelHeight * 0.8f), 
+                                    origin.y - (index + 0.9f) * keyPixelHeight,
+                                    keyPixelWidth * value, keyPixelHeight * 0.8f),
                                 GUIContent.none, colorStyle[i % colorStyle.Length]);
                             valsCum += value;
 
         }
     }
 
+    /// Helper method used to initialize the GUI. Called once.
     void Initialize()
     {
         keyStyle = GUI.skin.label;
-        barColors = new Color[6]{ Color.magenta, Color.blue, Color.cyan, Color.green, Color.yellow, Color.red };
+        barColors = new Color[6] { Color.magenta, Color.blue, Color.cyan, Color.green, Color.yellow, Color.red };
         colorStyle = new GUIStyle[barColors.Length];
         for (int i = 0; i < barColors.Length; i++)
         {

unity-environment/ProjectSettings/TagManager.asset

   - blueGoal
   - blueAgent
   - redAgent
+  - redBlock
+  - goal
+  - ground
+  - orangeBlock
+  - block
+  - orangeGoal
   layers:
   - Default
   - TransparentFX

docs/Feature-On-Demand-Decisions.md

+# On Demand Decision Making
+
+## Description
+On demand decision making allows agents to request decisions from their 
+brains only when needed instead of receiving decisions at a fixed 
+frequency. This is useful when the agents commit to an action for a 
+variable number of steps or when the agents cannot make decisions 
+at the same time. This typically the case for turn based games, games 
+where agents must react to events or games where agents can take 
+actions of variable duration.
+
+## How to use
+
+To enable or disable on demand decision making, use the checkbox called
+`On Demand Decisions` in the Agent Inspector.
+
+<p align="center">
+    <img src="images/ml-agents-ODD.png" 
+        alt="On Demand Decision" 
+        width="500" border="10" />
+</p>
+
+ * If `On Demand Decisions` is not checked, the Agent will request a new 
+ decision every `Decision Frequency` steps and 
+ perform an action every step. In the example above, 
+ `CollectObservations()` will be called every 5 steps and 
+ `AgentAction()` will be called at every step. This means that the 
+ Agent will reuse the decision the Brain has given it. 
+
+ * If `On Demand Decisions` is checked, the Agent controls when to receive
+ decisions, and take actions. To do so, the Agent may leverage one or two methods:
+   * `RequestDecision()` Signals that the Agent is requesting a decision.
+   This causes the Agent to collect its observations and ask the Brain for a 
+   decision at the next step of the simulation. Note that when an Agent 
+   requests a decision, it also request an action. 
+   This is to ensure that all decisions lead to an action during training.
+   * `RequestAction()` Signals that the Agent is requesting an action. The
+   action provided to the Agent in this case is the same action that was
+   provided the last time it requested a decision. 

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.
+
+## 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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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Scripts/RayPerception.cs

+using System.Collections.Generic;
+using UnityEngine;
+
+/// <summary>
+/// Ray perception component. Attach this to agents to enable "local perception"
+/// via the use of ray casts directed outward from the agent. 
+/// </summary>
+public class RayPerception : MonoBehaviour
+{
+    List<float> perceptionBuffer = new List<float>();
+    Vector3 endPosition;
+    RaycastHit hit;
+    /// <summary>
+    /// Creates perception vector to be used as part of an observation of an agent.
+    /// </summary>
+    /// <returns>The partial vector observation corresponding to the set of rays</returns>
+    /// <param name="rayDistance">Radius of rays</param>
+    /// <param name="rayAngles">Anlges of rays (starting from (1,0) on unit circle).</param>
+    /// <param name="detectableObjects">List of tags which correspond to object types agent can see</param>
+    /// <param name="startOffset">Starting heigh offset of ray from center of agent.</param>
+    /// <param name="endOffset">Ending height offset of ray from center of agent.</param>
+    public List<float> Perceive(float rayDistance,
+                         float[] rayAngles, string[] detectableObjects,
+                          float startOffset, float endOffset)
+    {
+        perceptionBuffer.Clear();
+        // For each ray sublist stores categorial information on detected object
+        // along with object distance.
+        foreach (float angle in rayAngles)
+        {
+            endPosition = transform.TransformDirection(
+                PolarToCartesian(rayDistance, angle));
+            endPosition.y = endOffset;
+            if (Application.isEditor)
+            {
+                Debug.DrawRay(transform.position + new Vector3(0f, startOffset, 0f),
+              endPosition, Color.black, 0.01f, true);
+            }
+            float[] subList = new float[detectableObjects.Length + 2];
+            if (Physics.SphereCast(transform.position +
+                                   new Vector3(0f, startOffset, 0f), 0.5f,
+                                   endPosition, out hit, rayDistance))
+            {
+                for (int i = 0; i < detectableObjects.Length; i++)
+                {
+                    if (hit.collider.gameObject.CompareTag(detectableObjects[i]))
+                    {
+                        subList[i] = 1;
+                        subList[detectableObjects.Length + 1] = hit.distance / rayDistance;
+                        break;
+                    }
+                }
+            }
+            else
+            {
+                subList[detectableObjects.Length] = 1f;
+            }
+            perceptionBuffer.AddRange(subList);
+        }
+        return perceptionBuffer;
+    }
+
+    /// <summary>
+    /// Converts polar coordinate to cartesian coordinate.
+    /// </summary>
+    public static Vector3 PolarToCartesian(float radius, float angle)
+    {
+        float x = radius * Mathf.Cos(DegreeToRadian(angle));
+        float z = radius * Mathf.Sin(DegreeToRadian(angle));
+        return new Vector3(x, 0f, z);
+    }
+
+    /// <summary>
+    /// Converts degrees to radians.
+    /// </summary>
+    public static float DegreeToRadian(float degree)
+    {
+        return degree * Mathf.PI / 180f;
+    }
+}

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

+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+
+public class WallJumpAcademy : Academy {
+
+    [Header("Specific to WallJump")]
+	public float agentRunSpeed;
+    public float agentJumpHeight;
+    //when a goal is scored the ground will use this material for a few seconds.
+	public Material goalScoredMaterial; 
+    //when fail, the ground will use this material for a few seconds. 
+    public Material failMaterial;
+
+    [HideInInspector]
+    //use ~3 to make things less floaty
+	public float gravityMultiplier = 2.5f; 
+    [HideInInspector]
+	public float agentJumpVelocity = 777;
+    [HideInInspector]
+	public float agentJumpVelocityMaxChange = 10;
+
+    // Use this for initialization
+    public override void InitializeAcademy()
+    {
+        Physics.gravity *= gravityMultiplier;
+    }
+}

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

+//Put this script on your blue cube.
+
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+using System.Linq;
+
+public class WallJumpAgent : Agent
+{
+    // Depending on this value, the wall will have different height
+    int configuration;
+    // Brain to use when no wall is present
+    public Brain noWallBrain;
+    // Brain to use when a jumpable wall is present
+    public Brain smallWallBrain;
+    // Brain to use when a wall requiring a block to jump over is present
+    public Brain bigWallBrain;
+
+    public GameObject ground;
+    public GameObject spawnArea;
+    Bounds spawnAreaBounds;
+
+
+    public GameObject goal;
+    public GameObject shortBlock;
+    public GameObject wall;
+    Rigidbody shortBlockRB;
+    Rigidbody agentRB;
+    Material groundMaterial;
+    Renderer groundRenderer;
+    WallJumpAcademy academy;
+    RayPerception rayPer;
+
+    public float jumpingTime;
+    public float jumpTime;
+    // This is a downward force applied when falling to make jumps look
+    // less floaty
+    public float fallingForce;
+    // Use to check the coliding objects
+    public Collider[] hitGroundColliders = new Collider[3];
+    Vector3 jumpTargetPos;
+    Vector3 jumpStartingPos;
+
+    string[] detectableObjects;
+
+    public override void InitializeAgent()
+    {
+        academy = FindObjectOfType<WallJumpAcademy>();
+        rayPer = GetComponent<RayPerception>();
+        configuration = Random.Range(0, 5);
+        detectableObjects = new string[] { "wall", "goal", "block" };
+
+        agentRB = GetComponent<Rigidbody>();
+        shortBlockRB = shortBlock.GetComponent<Rigidbody>();
+        spawnAreaBounds = spawnArea.GetComponent<Collider>().bounds;
+        groundRenderer = ground.GetComponent<Renderer>();
+        groundMaterial = groundRenderer.material;
+
+        spawnArea.SetActive(false);
+    }
+
+
+    // Begin the jump sequence
+    public void Jump()
+    {
+
+        jumpingTime = 0.2f;
+        jumpStartingPos = agentRB.position;
+    }
+
+    /// <summary>
+    /// Does the ground check.
+    /// </summary>
+    /// <returns><c>true</c>, if the agent is on the ground, 
+    /// <c>false</c> otherwise.</returns>
+    /// <param name="boxWidth">The width of the box used to perform 
+    /// the ground check. </param>
+    public bool DoGroundCheck(float boxWidth)
+    {
+        hitGroundColliders = new Collider[3];
+        Physics.OverlapBoxNonAlloc(
+            gameObject.transform.position + new Vector3(0, -0.05f, 0),
+                            new Vector3(boxWidth / 2f, 0.5f, boxWidth / 2f),
+                            hitGroundColliders,
+                            gameObject.transform.rotation);
+        bool grounded = false;
+        foreach (Collider col in hitGroundColliders)
+        {
+
+            if (col != null && col.transform != this.transform &&
+                (col.CompareTag("walkableSurface") ||
+                col.CompareTag("block") ||
+                 col.CompareTag("wall")))
+            {
+                grounded = true; //then we're grounded
+                break;
+            }
+        }
+        return grounded;
+    }
+
+
+    /// <summary>
+    /// Moves  a rigidbody towards a position smoothly.
+    /// </summary>
+    /// <param name="targetPos">Target position.</param>
+    /// <param name="rb">The rigidbody to be moved.</param>
+    /// <param name="targetVel">The velocity to target during the
+    ///  motion.</param>
+    /// <param name="maxVel">The maximum velocity posible.</param>
+    void MoveTowards(
+        Vector3 targetPos, Rigidbody rb, float targetVel, float maxVel)
+    {
+        Vector3 moveToPos = targetPos - rb.worldCenterOfMass;
+        Vector3 velocityTarget = moveToPos * targetVel * Time.fixedDeltaTime;
+        if (float.IsNaN(velocityTarget.x) == false)
+        {
+            rb.velocity = Vector3.MoveTowards(
+                rb.velocity, velocityTarget, maxVel);
+        }
+    }
+
+    public override void CollectObservations()
+    {
+        float rayDistance = 20f;
+        float[] rayAngles = { 0f, 45f, 90f, 135f, 180f, 110f, 70f };
+        AddVectorObs(rayPer.Perceive(
+            rayDistance, rayAngles, detectableObjects, 0f, 0f));
+        AddVectorObs(rayPer.Perceive(
+            rayDistance, rayAngles, detectableObjects, 2.5f, 2.5f));
+        Vector3 agentPos = agentRB.position - ground.transform.position;
+
+        AddVectorObs(agentPos / 20f);
+        AddVectorObs(DoGroundCheck(0.4f) ? 1 : 0);
+
+
+    }
+
+    /// <summary>
+    /// Gets a random spawn position in the spawningArea.
+    /// </summary>
+    /// <returns>The random spawn position.</returns>
+    public Vector3 GetRandomSpawnPos()
+    {
+        Vector3 randomSpawnPos = Vector3.zero;
+        float randomPosX = Random.Range(-spawnAreaBounds.extents.x,
+                                        spawnAreaBounds.extents.x);
+        float randomPosZ = Random.Range(-spawnAreaBounds.extents.z,
+                                        spawnAreaBounds.extents.z);
+
+        randomSpawnPos = spawnArea.transform.position +
+                                  new Vector3(randomPosX, 0.45f, randomPosZ);
+        return randomSpawnPos;
+    }
+
+    /// <summary>
+    /// Chenges the color of the ground for a moment
+    /// </summary>
+    /// <returns>The Enumerator to be used in a Coroutine</returns>
+    /// <param name="mat">The material to be swaped.</param>
+    /// <param name="time">The time the material will remain.</param>
+    IEnumerator GoalScoredSwapGroundMaterial(Material mat, float time)
+    {
+        groundRenderer.material = mat;
+        yield return new WaitForSeconds(time); //wait for 2 sec
+        groundRenderer.material = groundMaterial;
+    }
+
+
+    public void MoveAgent(float[] act)
+    {
+
+        AddReward(-0.0005f);
+        bool smallGrounded = DoGroundCheck(0.4f);
+        bool largeGrounded = DoGroundCheck(1.0f);
+
+        Vector3 dirToGo = Vector3.zero;
+        Vector3 rotateDir = Vector3.zero;
+
+        int action = Mathf.FloorToInt(act[0]);
+        switch (action)
+        {
+            case 0:
+                dirToGo = transform.forward * 1f * (largeGrounded ? 1f : 0.5f);
+                break;
+            case 1:
+                dirToGo = transform.forward * -1f * (largeGrounded ? 1f : 0.5f);
+                break;
+            case 2:
+
+                rotateDir = transform.up * -1f;
+                break;
+            case 3:
+                rotateDir = transform.up * 1f;
+                break;
+            case 4:
+                dirToGo = transform.right * -0.6f * (largeGrounded ? 1f : 0.5f);
+                break;
+            case 5:
+                dirToGo = transform.right * 0.6f * (largeGrounded ? 1f : 0.5f);
+                break;
+            case 6:
+                if ((jumpingTime <= 0f) && smallGrounded)
+                {
+                    Jump();
+                }
+                break;
+        }
+
+        transform.Rotate(rotateDir, Time.fixedDeltaTime * 300f);
+        agentRB.AddForce(dirToGo * academy.agentRunSpeed,
+                         ForceMode.VelocityChange);
+
+        if (jumpingTime > 0f)
+        {
+            jumpTargetPos =
+            new Vector3(agentRB.position.x,
+                        jumpStartingPos.y + academy.agentJumpHeight,
+                        agentRB.position.z) + dirToGo;
+            MoveTowards(jumpTargetPos, agentRB, academy.agentJumpVelocity,
+                        academy.agentJumpVelocityMaxChange);
+
+        }
+
+        if (!(jumpingTime > 0f) && !largeGrounded)
+        {
+            agentRB.AddForce(
+            Vector3.down * fallingForce, ForceMode.Acceleration);
+        }
+        jumpingTime -= Time.fixedDeltaTime;
+    }
+
+    public override void AgentAction(float[] vectorAction, string textAction)
+    {
+        MoveAgent(vectorAction);
+        if ((!Physics.Raycast(agentRB.position, Vector3.down, 20))
+            || (!Physics.Raycast(shortBlockRB.position, Vector3.down, 20)))
+        {
+            Done();
+            SetReward(-1f);
+            ResetBlock(shortBlockRB);
+            StartCoroutine(
+                GoalScoredSwapGroundMaterial(academy.failMaterial, .5f));
+        }
+    }
+
+    // Detect when the agent hits the goal
+    void OnTriggerEnter(Collider col)
+    {
+        if (col.gameObject.CompareTag("goal") && DoGroundCheck(0.4f))
+        {
+            SetReward(1f);
+            Done();
+            StartCoroutine(
+                GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 2));
+        }
+    }
+
+
+    //Reset the orange block position
+    void ResetBlock(Rigidbody blockRB)
+    {
+        blockRB.transform.position = GetRandomSpawnPos();
+        blockRB.velocity = Vector3.zero;
+        blockRB.angularVelocity = Vector3.zero;
+    }
+
+    public override void AgentReset()
+    {
+        ResetBlock(shortBlockRB);
+        transform.localPosition = new Vector3(
+            18 * (Random.value - 0.5f), 1, -12);
+        configuration = Random.Range(0, 5);
+        agentRB.velocity = default(Vector3);
+
+    }
+
+    private void FixedUpdate()
+    {
+        if (configuration != -1)
+        {
+            ConfigureAgent(configuration);
+            configuration = -1;
+        }
+    }
+
+    /// <summary>
+    /// Configures the agent. Given an integer config, the wall will have
+    /// different height and a different brain will be assigned to the agent.
+    /// </summary>
+    /// <param name="config">Config. 
+    /// If 0 : No wall and noWallBrain.
+    /// If 1:  Small wall and smallWallBrain.
+    /// Other : Tall wall and BigWallBrain. </param>
+    void ConfigureAgent(int config)
+    {
+        if (config == 0)
+        {
+            wall.transform.localScale = new Vector3(
+                wall.transform.localScale.x,
+                academy.resetParameters["no_wall_height"],
+                wall.transform.localScale.z);
+            GiveBrain(noWallBrain);
+        }
+        else if (config == 1)
+        {
+            wall.transform.localScale = new Vector3(
+                wall.transform.localScale.x,
+                academy.resetParameters["small_wall_height"],
+                wall.transform.localScale.z);
+            GiveBrain(smallWallBrain);
+        }
+        else
+        {
+            float height =
+                academy.resetParameters["big_wall_min_height"] +
+                Random.value * (academy.resetParameters["big_wall_max_height"] -
+                academy.resetParameters["big_wall_min_height"]);
+            wall.transform.localScale = new Vector3(
+                wall.transform.localScale.x,
+                height,
+                wall.transform.localScale.z);
+            GiveBrain(bigWallBrain);
+        }
+    }
+}
+

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