Release v0.8 docs

README.md

@@ -34,6 +34,8 @@ developer communities.
 * Visualizing network outputs within the environment
 * Simplified set-up with Docker
 * Wrap learning environments as a gym
+* Utilizes the Unity Inference Engine
+* Train using concurrent Unity environment instances
 
 ## Documentation
 

docs/Custom-Protos.md → docs/Creating-Custom-Protobuf-Messages.md

@@ -1,28 +1,30 @@
-# Creating custom protobuf messages
+# Creating Custom Protobuf Messages
 
 Unity and Python communicate by sending protobuf messages to and from each other. You can create custom protobuf messages if you want to exchange structured data beyond what is included by default. 
 
-Assume the ml-agents repository is checked out to a folder named $MLAGENTS_ROOT. Whenever you change the fields of a custom message, you must run `$MLAGENTS_ROOT/protobuf-definitions/make.bat` to create C# and Python files corresponding to the new message. Follow the directions in [this file](../protobuf-definitions/README.md) for guidance. After running it, reinstall the Python package by running `pip install $MLAGENTS_ROOT/ml-agents` and make sure your Unity project is using the newly-generated version of `$MLAGENTS_ROOT/UnitySDK`.
+## Implementing a Custom Message
 
-## Custom message types
+Assume the ml-agents repository is checked out to a folder named $MLAGENTS_ROOT. Whenever you change the fields of a custom message, you must run `$MLAGENTS_ROOT/protobuf-definitions/make.bat` to create C# and Python files corresponding to the new message. Follow the directions in [this file](../protobuf-definitions/README.md) for guidance. After running `$MLAGENTS_ROOT/protobuf-definitions/make.bat`, reinstall the Python package by running `pip install $MLAGENTS_ROOT/ml-agents` and make sure your Unity project is using the newly-generated version of `$MLAGENTS_ROOT/UnitySDK`.
 
-There are three custom message types currently supported, described below. In each case, `env` is an instance of a `UnityEnvironment` in Python. `CustomAction` is described most thoroughly; usage of the  other custom messages follows a similar template. 
+## Custom Message Types
 
-### Custom actions
+There are three custom message types currently supported - Custom Actions, Custom Reset Parameters, and Custom Observations. In each case, `env` is an instance of a `UnityEnvironment` in Python.
 
-By default, the Python API sends actions to Unity in the form of a floating-point list per agent and an optional string-valued text action. 
+### Custom Actions
 
-You can define a custom action type to replace or augment this by adding fields to the `CustomAction` message, which you can do by editing the file `protobuf-definitions/proto/mlagents/envs/communicator_objects/custom_action.proto`. 
+By default, the Python API sends actions to Unity in the form of a floating point list and an optional string-valued text action for each agent.
 
-Instances of custom actions are set via the `custom_action` parameter of `env.step`. An agent receives a custom action by defining a method with the signature
+You can define a custom action type, to either replace or augment the default, by adding fields to the `CustomAction` message, which you can do by editing the file `protobuf-definitions/proto/mlagents/envs/communicator_objects/custom_action.proto`. 
+
+Instances of custom actions are set via the `custom_action` parameter of the `env.step`. An agent receives a custom action by defining a method with the signature:
 
 ```csharp
 public virtual void AgentAction(float[] vectorAction, string textAction, CommunicatorObjects.CustomAction customAction)
 ```
 
-Here is an example of creating a custom action that instructs an agent to choose a cardinal direction to walk in and how far to walk. 
+Below is an example of creating a custom action that instructs an agent to choose a cardinal direction to walk in and how far to walk. 
 
-`custom_action.proto` will look like 
+The `custom_action.proto` file looks like:
 
 ```protobuf
 syntax = "proto3";
@@ -42,7 +44,7 @@ message CustomAction {
 }
 ```
 
-In your Python file, create an instance of a custom action:
+The Python instance of the custom action looks like:
 
 ```python
 from mlagents.envs.communicator_objects import CustomAction
@@ -52,7 +54,7 @@ action = CustomAction(direction=CustomAction.NORTH, walkAmount=2.0)
 env.step(custom_action=action)
 ```
 
-Then in your agent,
+And the agent code looks like:
 
 ```csharp
 ...
@@ -72,17 +74,17 @@ class MyAgent : Agent {
 }
 ```
 
-Note that the protobuffer compiler automatically configures the capitalization scheme of the C# version of the custom field names you defined in the `CustomAction` message to match C# conventions - "NORTH" becomes "North", "walkAmount" becomes "WalkAmount", etc.
+Keep in mind that the protobuffer compiler automatically configures the capitalization scheme of the C# version of the custom field names you defined in the `CustomAction` message to match C# conventions - "NORTH" becomes "North", "walkAmount" becomes "WalkAmount", etc.
 
-### Custom reset parameters
+### Custom Reset Parameters
 
-By default, you can configure an environment `env ` in the Python API by specifying a `config` parameter that is a dictionary mapping strings to floats. 
+By default, you can configure an environment `env` in the Python API by specifying a `config` parameter that is a dictionary mapping strings to floats. 
 
-You can also configure an environment using a custom protobuf message. To do so, add fields to the `CustomResetParameters` protobuf message in `custom_reset_parameters.proto`, analogously to `CustomAction` above. Then pass an instance of the message to `env.reset` via the `custom_reset_parameters` keyword parameter.
+You can also configure the environment reset using a custom protobuf message. To do this, add fields to the `CustomResetParameters` protobuf message in `custom_reset_parameters.proto`, analogously to `CustomAction` above. Then pass an instance of the message to `env.reset` via the `custom_reset_parameters` keyword parameter.
 
 In Unity, you can then access the `customResetParameters` field of your academy to accesss the values set in your Python script.
 
-In this example, an academy is setting the initial position of a box based on custom reset parameters that looks like 
+In this example, the academy is setting the initial position of a box based on custom reset parameters.  The `custom_reset_parameters.proto` would look like:
 
 ```protobuf
 message CustomResetParameters {
@@ -101,7 +103,18 @@ message CustomResetParameters {
 }
 ```
 
-In your academy, you'd have something like
+The Python instance of the custom reset parameter looks like
+
+```python
+from mlagents.envs.communicator_objects import CustomResetParameters
+env = ...
+pos = CustomResetParameters.Position(x=1, y=1, z=2)
+color = CustomResetParameters.Color(r=.5, g=.1, b=1.0)
+params = CustomResetParameters(initialPos=pos, color=color)
+env.reset(custom_reset_parameters=params)
+```
+
+The academy looks like
 
 ```csharp
 public class MyAcademy : Academy
@@ -122,18 +135,7 @@ public class MyAcademy : Academy
 }
 ```
 
-Then in Python, when setting up your scene, you might write
-
-```python
-from mlagents.envs.communicator_objects import CustomResetParameters
-env = ...
-pos = CustomResetParameters.Position(x=1, y=1, z=2)
-color = CustomResetParameters.Color(r=.5, g=.1, b=1.0)
-params = CustomResetParameters(initialPos=pos, color=color)
-env.reset(custom_reset_parameters=params)
-```
-
-### Custom observations
+### Custom Observations
 
 By default, Unity returns observations to Python in the form of a floating-point vector. 
 
@@ -143,8 +145,7 @@ Then in your agent, create an instance of a custom observation via `new Communic
 
 In Python, the custom observation can be accessed by calling `env.step` or `env.reset` and accessing the `custom_observations` property of the return value. It will contain a list with one `CustomObservation` instance per agent.
 
-For example, if you have added a field called `customField` to the `CustomObservation` message, you would program your agent like
-
+For example, if you have added a field called `customField` to the `CustomObservation` message, the agent code looks like:
 
 ```csharp
 class MyAgent : Agent {
@@ -156,7 +157,7 @@ class MyAgent : Agent {
 }
 ```
 
-Then in Python, the custom field would be accessed like 
+In Python, the custom field would be accessed like:
 
 ```python
 ...

docs/Installation.md

@@ -82,8 +82,7 @@ parameters you can use with `mlagents-learn`.
 
 If you intend to make modifications to `ml-agents` or `ml-agents-envs`, you should install 
 the packages from the cloned repo rather than from PyPi. To do this, you will need to install
- `ml-agents` and `ml-agents-envs` separately. Do this by running (starting from the repo's main
- directory):
+ `ml-agents` and `ml-agents-envs` separately. From the repo's root directory, run:
 
 ```sh
 cd ml-agents-envs
@@ -98,7 +97,6 @@ reflected when you run `mlagents-learn`. It is important to install these packag
 `mlagents` package depends on `mlagents_envs`, and installing it in the other 
 order will download `mlagents_envs` from PyPi. 
 
-
 ## Docker-based Installation
 
 If you'd like to use Docker for ML-Agents, please follow

docs/Migrating.md

@@ -1,5 +1,22 @@
 # Migrating
 
+## Migrating from ML-Agents toolkit v0.7 to v0.8
+
+### Important Changes
+* We have split the Python packges into two seperate packages `ml-agents` and `ml-agents-envs`
+
+#### Steps to Migrate
+* If you are installing via PyPI, there is no change.
+* If you intend to make modifications to `ml-agents` or `ml-agents-envs` please check the Installing for Development in the [Installation documentation](Installation.md).
+
+## Migrating from ML-Agents toolkit v0.6 to v0.7
+
+### Important Changes
+* We no longer support TFS and are now using the [Unity Inference Engine](Unity-Inference-Engine.md)
+
+#### Steps to Migrate
+* Make sure to remove the `ENABLE_TENSORFLOW` flag in your Unity Project settings
+
 ## Migrating from ML-Agents toolkit v0.5 to v0.6
 
 ### Important Changes

docs/Readme.md

@@ -29,6 +29,7 @@
 * [Learning Environment Best Practices](Learning-Environment-Best-Practices.md)
 * [Using the Monitor](Feature-Monitor.md)
 * [Using an Executable Environment](Learning-Environment-Executable.md)
+* [Creating Custom Protobuf Messages](Creating-Custom-Protobuf-Messages.md)
 
 ## Training
 
@@ -39,6 +40,7 @@
 * [Training with LSTM](Feature-Memory.md)
 * [Training on the Cloud with Amazon Web Services](Training-on-Amazon-Web-Service.md)
 * [Training on the Cloud with Microsoft Azure](Training-on-Microsoft-Azure.md)
+* [Training Using Concurrent Unity Instances](Training-Using-Concurrent-Unity-Instances.md)
 * [Using TensorBoard to Observe Training](Using-Tensorboard.md)
 
 ## Inference

docs/Training-ML-Agents.md

@@ -134,12 +134,9 @@ environment, you can set the following command line options when invoking
   [Academy Properties](Learning-Environment-Design-Academy.md#academy-properties).
 * `--train` – Specifies whether to train model or only run in inference mode.
   When training, **always** use the `--train` option.
-* `--num-envs` - Specifies the number of parallel environments to collect
+* `--num-envs=<n>` - Specifies the number of concurrent Unity environment instances to collect
   experiences from when training. Defaults to 1.
-* `--base-port` - Specifies the starting port for environment workers. Each Unity
-  environment will use the port `(base_port + worker_id)`, where the worker ID
-  are sequential IDs given to each environment from 0 to `num_envs - 1`.
-  Defaults to 5005.
+* `--base-port` - Specifies the starting port. Each concurrent Unity environment instance will get assigned a port sequentially, starting from the `base-port`.  Each instance will use the port `(base_port + worker_id)`, where the `worker_id` is sequential IDs given to each instance from 0 to `num_envs - 1`. Default is 5005.
 * `--docker-target-name=<dt>` – The Docker Volume on which to store curriculum,
   executable and model files. See [Using Docker](Using-Docker.md).
 * `--no-graphics` - Specify this option to run the Unity executable in

docs/Training-Using-Concurrent-Unity-Instances.md

@@ -0,0 +1,25 @@
+# Training Using Concurrent Unity Instances
+
+As part of release v0.8, we enabled developers to run concurrent, parallel instances of the Unity executable during training. For certain scenarios, this should speed up the training.  
+
+## How to Run Concurrent Unity Instances During Training
+
+Please refer to the general instructions on [Training ML-Agents](Training-ML-Agents.md).  In order to run concurrent Unity instances during training, set the number of environment instances using the command line option `--num-envs=<n>` when you invoke `mlagents-learn`. Optionally, you can also set the `--base-port`, which is the starting port used for the concurrent Unity instances.
+
+## Considerations
+
+### Buffer Size
+
+If you are having trouble getting an agent to train, even with multiple concurrent Unity instances, you could increase  `buffer_size` in the `config/trainer_config.yaml` file. A common practice is to multiply `buffer_size` by `num-envs`.
+
+### Resource Constraints
+
+Invoking concurrent Unity instances is constrained by the resources on the machine.  Please use discretion when setting `--num-envs=<n>`.
+
+### Using num-runs and num-envs
+
+If you set `--num-runs=<n>` greater than 1 and are also invoking concurrent Unity instances using `--num-envs=<n>`, then the number of concurrent Unity instances is equal to `num-runs` times `num-envs`.
+
+### Result Variation Using Concurrent Unity Instances
+
+If you keep all the hyperparameters the same, but change `--num-envs=<n>`, the results and model would likely change.