fix trailing whitespace in markdown (#2786)

Author: Chris Elion

.pre-commit-config.yaml

@@ -45,6 +45,9 @@ repos:
                 .*_pb2_grpc.py
             )$
         additional_dependencies: [flake8-comprehensions]
+    -   id: trailing-whitespace
+        name: trailing-whitespace-markdown
+        types: [markdown]
 
 -   repo: https://github.com/pre-commit/pygrep-hooks
     rev: v1.4.1  # Use the ref you want to point at

SURVEY.md

@@ -1,5 +1,5 @@
 # Unity ML-Agents Toolkit Survey
 
-Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it. 
+Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it.
 
-[Fill out the survey](https://goo.gl/forms/qFMYSYr5TlINvG6f1) 
+[Fill out the survey](https://goo.gl/forms/qFMYSYr5TlINvG6f1)

UnitySDK/Assets/ML-Agents/Plugins/Barracuda.Core/Barracuda.md

@@ -249,7 +249,7 @@ Tan
 Tanh
 ```
 
-P.S. some of these operations are under limited support and not all configurations are properly supported 
+P.S. some of these operations are under limited support and not all configurations are properly supported
 
 P.P.S. Python 3.5 or 3.6 is recommended
 

UnitySDK/Assets/ML-Agents/Plugins/Barracuda.Core/LICENSE.md

@@ -1,6 +1,6 @@
 Barracuda cross-platform Neural Net engine copyright © 2018 Unity Technologies ApS
 
-Licensed under the Unity Companion License for Unity-dependent projects--see [Unity Companion License](http://www.unity3d.com/legal/licenses/Unity_Companion_License). 
+Licensed under the Unity Companion License for Unity-dependent projects--see [Unity Companion License](http://www.unity3d.com/legal/licenses/Unity_Companion_License).
 
 Unless expressly provided otherwise, the Software under this license is made available strictly on an “AS IS” BASIS WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. Please review the license for details on these and other terms and conditions.
 

UnitySDK/Assets/ML-Agents/Plugins/Barracuda.Core/ReleaseNotes.md

@@ -29,15 +29,15 @@
 - TF importer: made detection of actual output node from LSTM/GRU pattern more bullet proof by skipping Const nodes.
 - TF importer: improved InstanceNormalization handling.
 - TF importer: fixed SquareDifference pattern.
-- TF importer: fixed Conv2DBackpropInput (transpose convolution) import. 
+- TF importer: fixed Conv2DBackpropInput (transpose convolution) import.
 - Fixed Conv2D performance regression on some GPUs.
 - Fixed TextureAsTensorData.Download() to work properly with InterpretDepthAs.Channels.
 - Fixed bug when identity/nop layers would reuse input as an output and later causing premature release of that tensor as part of intermediate data cleanup.
 - Added scale + bias to TenstorToRenderTexture interface, usefull for adjusting network output scale + bias on the fly.
 - Fixed double Dispose issue when worker gets garbage collected.
 
 ## 0.2.0
-- Version bumped to 0.2.0 as it brings breaking API changes, for details look below. 
+- Version bumped to 0.2.0 as it brings breaking API changes, for details look below.
 - Significantly reduced temporary memory allocations by introducing internal allocator support. Now memory is re-used between layer execution as much as possible.
 - Improved small workload performance on CSharp backend
 - Added parallel implementation for multiple activation functions on CSharp backend
@@ -47,7 +47,7 @@
 - Added `Summary()` method to `Worker`. Currently returns allocator information.
 - Tabs to spaces! Aiming at higher salary (https://stackoverflow.blog/2017/06/15/developers-use-spaces-make-money-use-tabs/).
 - Renamed worker type enum members: `CSharp` -> `CSharpRef`, `CSharpFast` -> `CSharp`, `Compute` -> `ComputeRef`, `ComputeFast` -> `Compute`.
-- Implemented new optimized `ComputePrecompiled` worker. This worker caches Compute kernels and state beforehand to reduce CPU overhead. 
+- Implemented new optimized `ComputePrecompiled` worker. This worker caches Compute kernels and state beforehand to reduce CPU overhead.
 - Added `ExecuteAsync()` to `IWorker` interface, it returns `IEnumerator`, which enables you to control how many layers to schedule per frame (one iteration == one layer).
 - Added `Log` op support on Compute workers.
 - Optimized activation functions and ScaleBias by accessing tensor as continuous array. Gained ~2.0ms on 4 batch MobileNet (MBP2016).
@@ -123,8 +123,8 @@
 - Fixed compilation issues on Xbox One.
 - TexConv2D support was temporary disabled.
 - Barracuda logging now can be configured via static fields of ``Barracuda.D`` class, it allows both disable specific logging levels or just disable stack trace collection (helps with performance when profiling).
-- Compute Concat implementation now will fall back to C# implementation instead of throwing exception when unsupported configuration is encountered. 
-- Fixed several ``ComputeBuffer`` release issues. 
+- Compute Concat implementation now will fall back to C# implementation instead of throwing exception when unsupported configuration is encountered.
+- Fixed several ``ComputeBuffer`` release issues.
 - Added constructor for ``Tensor`` that allows to pass in data array.
 - Improved Flatten handling in TensorFlow models.
 - Added helper func ``ModelLoader.LoadFromStreamingAssets``.

UnitySDK/README.md

@@ -1,5 +1,5 @@
 # Unity ML-Agents SDK
 
-Contains the ML-Agents Unity Project, including 
-both the core plugin (in `Scripts`), as well as a set 
+Contains the ML-Agents Unity Project, including
+both the core plugin (in `Scripts`), as well as a set
 of example environments (in `Examples`).

docs/Basic-Guide.md

@@ -11,7 +11,7 @@ the basic concepts of Unity.
 ## Setting up the ML-Agents Toolkit within Unity
 
 In order to use the ML-Agents toolkit within Unity, you first need to change a few
-Unity settings. 
+Unity settings.
 
 1. Launch Unity
 2. On the Projects dialog, choose the **Open** option at the top of the window.
@@ -27,28 +27,28 @@ Unity settings.
 
 ## Running a Pre-trained Model
 
-We include pre-trained models for our agents (`.nn` files) and we use the 
-[Unity Inference Engine](Unity-Inference-Engine.md) to run these models 
-inside Unity. In this section, we will use the pre-trained model for the 
+We include pre-trained models for our agents (`.nn` files) and we use the
+[Unity Inference Engine](Unity-Inference-Engine.md) to run these models
+inside Unity. In this section, we will use the pre-trained model for the
 3D Ball example.
 
 1. In the **Project** window, go to the `Assets/ML-Agents/Examples/3DBall/Scenes` folder
    and open the `3DBall` scene file.
-2. In the **Project** window, go to the `Assets/ML-Agents/Examples/3DBall/Prefabs` folder. 
+2. In the **Project** window, go to the `Assets/ML-Agents/Examples/3DBall/Prefabs` folder.
    Expand `3DBall` and click on the `Agent` prefab.  You should see the `Agent` prefab in the **Inspector** window.
-   
+
    **Note**: The platforms in the `3DBall` scene were created using the `3DBall` prefab.  Instead of updating all 12 platforms individually, you can update the `3DBall` prefab instead.
-   
+
    ![Platform Prefab](images/platform_prefab.png)
-   
-3. In the **Project** window, drag the **3DBallLearning** Model located in 
+
+3. In the **Project** window, drag the **3DBallLearning** Model located in
    `Assets/ML-Agents/Examples/3DBall/TFModels` into the `Model` property under `Ball 3D Agent (Script)` component in the **Inspector** window.
-   
+
    ![3dball learning brain](images/3dball_learning_brain.png)
-   
-4. You should notice that each `Agent` under each `3DBall` in the **Hierarchy** windows now contains **3DBallLearning** as `Model`. __Note__ : You can modify multiple game objects in a scene by selecting them all at 
-   once using the search bar in the Scene Hierarchy. 
-8. Select the **InferenceDevice** to use for this model (CPU or GPU) on the Agent. 
+
+4. You should notice that each `Agent` under each `3DBall` in the **Hierarchy** windows now contains **3DBallLearning** as `Model`. __Note__ : You can modify multiple game objects in a scene by selecting them all at
+   once using the search bar in the Scene Hierarchy.
+8. Select the **InferenceDevice** to use for this model (CPU or GPU) on the Agent.
    _Note: CPU is faster for the majority of ML-Agents toolkit generated models_
 9. Click the **Play** button and you will see the platforms balance the balls
    using the pre-trained model.
@@ -71,13 +71,13 @@ More information and documentation is provided in the
 
 ### Setting up the environment for training
 
-In order to setup the Agents for Training, you will need to edit the 
+In order to setup the Agents for Training, you will need to edit the
 `Behavior Name` under `BehaviorParamters` in the Agent Inspector window.
 The `Behavior Name` is used to group agents per behaviors. Note that Agents
 sharing the same `Behavior Name` must be agents of the same type using the
-same `Behavior Parameters`. You can make sure all your agents have the same 
+same `Behavior Parameters`. You can make sure all your agents have the same
 `Behavior Parameters` using Prefabs.
-The `Behavior Name` corresponds to the name of the model that will be 
+The `Behavior Name` corresponds to the name of the model that will be
 generated by the training process and is used to select the hyperparameters
 from the training configuration file.
 

docs/Creating-Custom-Protobuf-Messages.md

@@ -3,7 +3,7 @@
 
 # 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. 
+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.
 
 ## Implementing a Custom Message
 
@@ -17,15 +17,15 @@ There are three custom message types currently supported - Custom Actions, Custo
 
 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.
 
-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`. 
+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)
 ```
 
-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. 
+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.
 
 The `custom_action.proto` file looks like:
 
@@ -42,7 +42,7 @@ message CustomAction {
         EAST=2;
         WEST=3;
     }
-    float walkAmount = 1;    
+    float walkAmount = 1;
     Direction direction = 2;
 }
 ```
@@ -81,7 +81,7 @@ Keep in mind that the protobuffer compiler automatically configures the capitali
 
 ### 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 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.
 
@@ -140,9 +140,9 @@ public class MyAcademy : Academy
 
 ### Custom Observations
 
-By default, Unity returns observations to Python in the form of a floating-point vector. 
+By default, Unity returns observations to Python in the form of a floating-point vector.
 
-You can define a custom observation message to supplement that. To do so, add fields to the `CustomObservation` protobuf message in `custom_observation.proto`. 
+You can define a custom observation message to supplement that. To do so, add fields to the `CustomObservation` protobuf message in `custom_observation.proto`.
 
 Then in your agent, create an instance of a custom observation via `new CommunicatorObjects.CustomObservation`. Then in `CollectObservations`, call `SetCustomObservation` with the custom observation instance as the parameter.
 
@@ -156,7 +156,7 @@ class MyAgent : Agent {
         var obs = new CustomObservation();
         obs.CustomField = 1.0;
         SetCustomObservation(obs);
-    }    
+    }
 }
 ```
 

docs/FAQ.md

@@ -44,7 +44,7 @@ UnityAgentsException: The Communicator was unable to connect. Please make sure t
 
 There may be a number of possible causes:
 
-* _Cause_: There may be no agent in the scene 
+* _Cause_: There may be no agent in the scene
 * _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

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

@@ -89,7 +89,7 @@ the 3D Balance Ball environment, the Agent components are placed on the twelve
 "Agent" GameObjects. The base Agent object has a few properties that affect its
 behavior:
 
-* **Behavior Parameters** — Every Agent must have a Behavior. The Behavior 
+* **Behavior Parameters** — Every Agent must have a Behavior. The Behavior
   determines how an Agent makes decisions. More on Behavior Parameters in
   the next section.
 * **Visual Observations** — Defines any Camera objects used by the Agent to
@@ -110,7 +110,7 @@ The Ball3DAgent subclass defines the following methods:
   training generalizes to more than a specific starting position and agent cube
   attitude.
 * agent.CollectObservations() — Called every simulation step. Responsible for
-  collecting the Agent's observations of the environment. Since the Behavior 
+  collecting the Agent's observations of the environment. Since the Behavior
   Parameters of the Agent are set with vector observation
   space with a state size of 8, the `CollectObservations()` must call
   `AddVectorObs` such that vector size adds up to 8.
@@ -122,11 +122,11 @@ The Ball3DAgent subclass defines the following methods:
   negative reward for dropping the ball. An Agent is also marked as done when it
   drops the ball so that it will reset with a new ball for the next simulation
   step.
-* agent.Heuristic() - When the `Use Heuristic` checkbox is checked in the Behavior 
+* agent.Heuristic() - When the `Use Heuristic` checkbox is checked in the Behavior
   Parameters of the Agent, the Agent will use the `Heuristic()` method to generate
   the actions of the Agent. As such, the `Heuristic()` method returns an array of
   floats. In the case of the Ball 3D Agent, the `Heuristic()` method converts the
-  keyboard inputs into actions.  
+  keyboard inputs into actions.
 
 
 #### Behavior Parameters : Vector Observation Space