Unity-Technologies · ervteng · Oct 21, 2020 · Sep 25, 2020 · Sep 25, 2020 · Sep 25, 2020
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -27,7 +27,7 @@ If applicable, add screenshots to help explain your problem.
 - Unity Version: [e.g. Unity 2020.1f1]
 - OS + version: [e.g. Windows 10]
 - _ML-Agents version_: (e.g. ML-Agents v0.8, or latest `develop` branch from source)
-- _TensorFlow version_: (you can run `pip3 show tensorflow` to get this)
+- _Torch version_: (you can run `pip3 show torch` to get this)
 - _Environment_: (which example environment you used to reproduce the error)
 
 **NOTE:** We are unable to help reproduce bugs with custom environments.  Please attempt to reproduce your issue with one of the example environments, or provide a minimal patch to one of the environments needed to reproduce the issue.
diff --git a/README.md b/README.md
@@ -13,7 +13,7 @@
 project that enables games and simulations to serve as environments for
 training intelligent agents. Agents can be trained using reinforcement learning,
 imitation learning, neuroevolution, or other machine learning methods through a
-simple-to-use Python API. We also provide implementations (based on TensorFlow)
+simple-to-use Python API. We also provide implementations (based on PyTorch)
 of state-of-the-art algorithms to enable game developers and hobbyists to easily
 train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be
 used for multiple purposes, including controlling NPC behavior (in a variety of

diff --git a/com.unity.ml-agents/CHANGELOG.md b/com.unity.ml-agents/CHANGELOG.md
@@ -12,6 +12,11 @@ and this project adheres to
 ### Major Changes
 #### com.unity.ml-agents (C#)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
+ - PyTorch trainers are now the default. See the
+ [installation docs](https://github.com/Unity-Technologies/ml-agents/blob/mastere/docs/Installation.md) for
+ more information on installing PyTorch. For the time being, TensorFlow is still available;
+ you can use the TensorFlow backend by adding `--tensorflow` to the CLI, or
+ adding `framework: tensorflow` in the configuration YAML. (#4517)
 
 ### Minor Changes
 #### com.unity.ml-agents (C#)

diff --git a/docs/Background-Machine-Learning.md b/docs/Background-Machine-Learning.md
@@ -194,4 +194,4 @@ we can learn policies for very complex environments (a complex environment is
 one where the number of observations an agent perceives and the number of
 actions they can take are large). Many of the algorithms we provide in ML-Agents
 use some form of deep learning, built on top of the open-source library,
-[TensorFlow](Background-TensorFlow.md).
+[PyTorch](Background-PyTorch.md).
diff --git a/docs/Background-TensorFlow.md → docs/Background-PyTorch.md b/docs/Background-TensorFlow.md → docs/Background-PyTorch.md
@@ -1,29 +1,29 @@
-# Background: TensorFlow
+# Background: PyTorch
 
 As discussed in our
 [machine learning background page](Background-Machine-Learning.md), many of the
 algorithms we provide in the ML-Agents Toolkit leverage some form of deep
 learning. More specifically, our implementations are built on top of the
-open-source library [TensorFlow](https://www.tensorflow.org/). In this page we
-provide a brief overview of TensorFlow, in addition to TensorFlow-related tools
+open-source library [PyTorch](https://pytorch.org/). In this page we
+provide a brief overview of PyTorch and TensorBoard
 that we leverage within the ML-Agents Toolkit.
 
-## TensorFlow
+## PyTorch
 
-[TensorFlow](https://www.tensorflow.org/) is an open source library for
+[PyTorch](https://pytorch.org/) is an open source library for
 performing computations using data flow graphs, the underlying representation of
 deep learning models. It facilitates training and inference on CPUs and GPUs in
 a desktop, server, or mobile device. Within the ML-Agents Toolkit, when you
-train the behavior of an agent, the output is a model (.nn) file that you can
+train the behavior of an agent, the output is a model (.onnx) file that you can
 then associate with an Agent. Unless you implement a new algorithm, the use of
-TensorFlow is mostly abstracted away and behind the scenes.
+PyTorch is mostly abstracted away and behind the scenes.
 
 ## TensorBoard
 
-One component of training models with TensorFlow is setting the values of
+One component of training models with PyTorch is setting the values of
 certain model attributes (called _hyperparameters_). Finding the right values of
 these hyperparameters can require a few iterations. Consequently, we leverage a
-visualization tool within TensorFlow called
+visualization tool called
 [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard).
 It allows the visualization of certain agent attributes (e.g. reward) throughout
 training which can be helpful in both building intuitions for the different

diff --git a/docs/Getting-Started.md b/docs/Getting-Started.md
@@ -91,7 +91,7 @@ itself to keep the ball balanced on its head.
 
 ## Running a pre-trained model
 
-We include pre-trained models for our agents (`.nn` files) and we use the
+We include pre-trained models for our agents (`.onnx` 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.
@@ -124,7 +124,7 @@ example.
 
 ## Training a new model with Reinforcement Learning
 
-While we provide pre-trained `.nn` files for the agents in this environment, any
+While we provide pre-trained models for the agents in this environment, any
 environment you make yourself will require training agents from scratch to
 generate a new model file. In this section we will demonstrate how to use the
 reinforcement learning algorithms that are part of the ML-Agents Python package
@@ -229,7 +229,7 @@ Once the training process completes, and the training process saves the model
 use it with compatible Agents (the Agents that generated the model). **Note:**
 Do not just close the Unity Window once the `Saved Model` message appears.
 Either wait for the training process to close the window or press `Ctrl+C` at
-the command-line prompt. If you close the window manually, the `.nn` file
+the command-line prompt. If you close the window manually, the `.onnx` file
 containing the trained model is not exported into the ml-agents folder.
 
 If you've quit the training early using `Ctrl+C` and want to resume training,
@@ -239,7 +239,7 @@ run the same command again, appending the `--resume` flag:
 mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun --resume
 ```
 
-Your trained model will be at `results/<run-identifier>/<behavior_name>.nn` where
+Your trained model will be at `results/<run-identifier>/<behavior_name>.onnx` where
 `<behavior_name>` is the name of the `Behavior Name` of the agents corresponding
 to the model. This file corresponds to your model's latest checkpoint. You can
 now embed this trained model into your Agents by following the steps below,
@@ -249,7 +249,7 @@ which is similar to the steps described [above](#running-a-pre-trained-model).
    `Project/Assets/ML-Agents/Examples/3DBall/TFModels/`.
 1. Open the Unity Editor, and select the **3DBall** scene as described above.
 1. Select the **3DBall** prefab Agent object.
-1. Drag the `<behavior_name>.nn` file from the Project window of the Editor to
+1. Drag the `<behavior_name>.onnx` file from the Project window of the Editor to
    the **Model** placeholder in the **Ball3DAgent** inspector window.
 1. Press the **Play** button at the top of the Editor.
 

diff --git a/docs/Installation.md b/docs/Installation.md
@@ -44,11 +44,6 @@ If your Python environment doesn't include `pip3`, see these
 [instructions](https://packaging.python.org/guides/installing-using-linux-tools/#installing-pip-setuptools-wheel-with-linux-package-managers)
 on installing it.
 
-Although we do not provide support for Anaconda installation on Windows, the
-previous
-[Windows Anaconda Installation (Deprecated) guide](Installation-Anaconda-Windows.md)
-is still available.
-
 ### Clone the ML-Agents Toolkit Repository (Optional)
 
 Now that you have installed Unity and Python, you can now install the Unity and
@@ -128,6 +123,7 @@ To install the `mlagents` Python package, activate your virtual environment and
 run from the command line:
 
 ```sh
+pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html
 pip3 install mlagents
 ```
 
@@ -138,7 +134,7 @@ line parameters you can use with `mlagents-learn`.
 
 By installing the `mlagents` package, the dependencies listed in the
 [setup.py file](../ml-agents/setup.py) are also installed. These include
-[TensorFlow](Background-TensorFlow.md) (Requires a CPU w/ AVX support).
+[PyTorch](Background-PyTorch.md) (Requires a CPU w/ AVX support).
 
 #### Advanced: Local Installation for Development
 
@@ -148,6 +144,7 @@ this, you will need to install `mlagents` and `mlagents_envs` separately. From
 the repository's root directory, run:
 
 ```sh
+pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html
 pip3 install -e ./ml-agents-envs
 pip3 install -e ./ml-agents
 ```

diff --git a/docs/Learning-Environment-Executable.md b/docs/Learning-Environment-Executable.md
@@ -171,7 +171,7 @@ INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 10000. Mean Reward: 2
 ```
 
 You can press Ctrl+C to stop the training, and your trained model will be at
-`results/<run-identifier>/<behavior_name>.nn`, which corresponds to your model's
+`results/<run-identifier>/<behavior_name>.onnx`, which corresponds to your model's
 latest checkpoint. (**Note:** There is a known bug on Windows that causes the
 saving of the model to fail when you early terminate the training, it's
 recommended to wait until Step has reached the max_steps parameter you set in
@@ -182,6 +182,6 @@ following the steps below:
    `Project/Assets/ML-Agents/Examples/3DBall/TFModels/`.
 1. Open the Unity Editor, and select the **3DBall** scene as described above.
 1. Select the **3DBall** prefab from the Project window and select **Agent**.
-1. Drag the `<behavior_name>.nn` file from the Project window of the Editor to
+1. Drag the `<behavior_name>.onnx` file from the Project window of the Editor to
    the **Model** placeholder in the **Ball3DAgent** inspector window.
 1. Press the **Play** button at the top of the Editor.
diff --git a/docs/ML-Agents-Overview.md b/docs/ML-Agents-Overview.md
@@ -35,7 +35,7 @@ open-source project that enables games and simulations to serve as environments
 for training intelligent agents. Agents can be trained using reinforcement
 learning, imitation learning, neuroevolution, or other machine learning methods
 through a simple-to-use Python API. We also provide implementations (based on
-TensorFlow) of state-of-the-art algorithms to enable game developers and
+PyTorch) of state-of-the-art algorithms to enable game developers and
 hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These
 trained agents can be used for multiple purposes, including controlling NPC
 behavior (in a variety of settings such as multi-agent and adversarial),
@@ -51,9 +51,9 @@ transition to the ML-Agents Toolkit easier, we provide several background pages
 that include overviews and helpful resources on the
 [Unity Engine](Background-Unity.md),
 [machine learning](Background-Machine-Learning.md) and
-[TensorFlow](Background-TensorFlow.md). We **strongly** recommend browsing the
+[PyTorch](Background-PyTorch.md). We **strongly** recommend browsing the
 relevant background pages if you're not familiar with a Unity scene, basic
-machine learning concepts or have not previously heard of TensorFlow.
+machine learning concepts or have not previously heard of PyTorch.
 
 The remainder of this page contains a deep dive into ML-Agents, its key
 components, different training modes and scenarios. By the end of it, you should
@@ -280,7 +280,7 @@ for additional information.
 
 ### Custom Training and Inference
 
-In the previous mode, the Agents were used for training to generate a TensorFlow
+In the previous mode, the Agents were used for training to generate a PyTorch
 model that the Agents can later use. However, any user of the ML-Agents Toolkit
 can leverage their own algorithms for training. In this case, the behaviors of
 all the Agents in the scene will be controlled within Python. You can even turn

diff --git a/docs/Readme.md b/docs/Readme.md
@@ -11,7 +11,7 @@
 - [ML-Agents Toolkit Overview](ML-Agents-Overview.md)
   - [Background: Unity](Background-Unity.md)
   - [Background: Machine Learning](Background-Machine-Learning.md)
-  - [Background: TensorFlow](Background-TensorFlow.md)
+  - [Background: PyTorch](Background-PyTorch.md)
 - [Example Environments](Learning-Environment-Examples.md)
 
 ## Creating Learning Environments

diff --git a/docs/Training-Configuration-File.md b/docs/Training-Configuration-File.md
@@ -32,7 +32,7 @@ choice of the trainer (which we review on subsequent sections).
 | `time_horizon`           | (default = `64`) How many steps of experience to collect per-agent before adding it to the experience buffer. When this limit is reached before the end of an episode, a value estimate is used to predict the overall expected reward from the agent's current state. As such, this parameter trades off between a less biased, but higher variance estimate (long time horizon) and more biased, but less varied estimate (short time horizon). In cases where there are frequent rewards within an episode, or episodes are prohibitively large, a smaller number can be more ideal. This number should be large enough to capture all the important behavior within a sequence of an agent's actions. <br><br> Typical range: `32` - `2048` |
 | `max_steps`              | (default = `500000`) Total number of steps (i.e., observation collected and action taken) that must be taken in the environment (or across all environments if using multiple in parallel) before ending the training process. If you have multiple agents with the same behavior name within your environment, all steps taken by those agents will contribute to the same `max_steps` count. <br><br>Typical range: `5e5` - `1e7`                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
 | `keep_checkpoints`         | (default = `5`) The maximum number of model checkpoints to keep. Checkpoints are saved after the number of steps specified by the checkpoint_interval option. Once the maximum number of checkpoints has been reached, the oldest checkpoint is deleted when saving a new checkpoint. |
-| `checkpoint_interval`         | (default = `500000`) The number of experiences collected between each checkpoint by the trainer. A maximum of `keep_checkpoints` checkpoints are saved before old ones are deleted. Each checkpoint saves the `.nn` (and `.onnx` if applicable) files in `results/` folder.|
+| `checkpoint_interval`         | (default = `500000`) The number of experiences collected between each checkpoint by the trainer. A maximum of `keep_checkpoints` checkpoints are saved before old ones are deleted. Each checkpoint saves the `.onnx` (and `.nn` if using TensorFlow) files in `results/` folder.|
 | `init_path`              | (default = None) Initialize trainer from a previously saved model. Note that the prior run should have used the same trainer configurations as the current run, and have been saved with the same version of ML-Agents. <br><br>You should provide the full path to the folder where the checkpoints were saved, e.g. `./models/{run-id}/{behavior_name}`. This option is provided in case you want to initialize different behaviors from different runs; in most cases, it is sufficient to use the `--initialize-from` CLI parameter to initialize all models from the same run.                                                                                                                                  |
 | `threaded`               | (default = `true`) By default, model updates can happen while the environment is being stepped. This violates the [on-policy](https://spinningup.openai.com/en/latest/user/algorithms.html#the-on-policy-algorithms) assumption of PPO slightly in exchange for a training speedup. To maintain the strict on-policyness of PPO, you can disable parallel updates by setting `threaded` to `false`. There is usually no reason to turn `threaded` off for SAC.                                                                                                                                                                                                                                                       |
 | `hyperparameters -> learning_rate`          | (default = `3e-4`) Initial learning rate for gradient descent. Corresponds to the strength of each gradient descent update step. This should typically be decreased if training is unstable, and the reward does not consistently increase. <br><br>Typical range: `1e-5` - `1e-3`                                                                                                                                                                                                                                                                                                                                                                                                                                                                |

diff --git a/docs/Training-ML-Agents.md b/docs/Training-ML-Agents.md
@@ -16,7 +16,6 @@
     - [Curriculum Learning](#curriculum)
       - [Training with a Curriculum](#training-with-a-curriculum)
   - [Training Using Concurrent Unity Instances](#training-using-concurrent-unity-instances)
-  - [Using PyTorch (Experimental)](#using-pytorch-experimental)
 
 For a broad overview of reinforcement learning, imitation learning and all the
 training scenarios, methods and options within the ML-Agents Toolkit, see
@@ -88,7 +87,7 @@ in the `results/<run-identifier>` folder:
    values. See [Using TensorBoard](Using-Tensorboard.md) for more details on how
    to visualize the training metrics.
 1. Models: these contain the model checkpoints that
-   are updated throughout training and the final model file (`.nn`). This final
+   are updated throughout training and the final model file (`.onnx`). This final
    model file is generated once either when training completes or is
    interrupted.
 1. Timers file (under `results/<run-identifier>/run_logs`): this contains aggregated
@@ -556,35 +555,3 @@ Some considerations:
 - **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.
-
-### Using PyTorch (Experimental)
-
-ML-Agents, by default, uses TensorFlow as its backend, but experimental support
-for PyTorch has been added. To use PyTorch, the `torch` Python package must
-be installed, and PyTorch must be enabled for your trainer.
-
-#### Installing PyTorch
-
-If you've already installed ML-Agents, follow the
-[official PyTorch install instructions](https://pytorch.org/get-started/locally/) for
-your platform and configuration. Note that on Windows, you may also need Microsoft's
-[Visual C++ Redistributable](https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads) if you don't have it already.
-
-If you're installing or upgrading ML-Agents on Linux or Mac, you can also run
-`pip3 install mlagents[torch]` instead of `pip3 install mlagents`
-during [installation](Installation.md). On Windows, install ML-Agents first and then
-separately install PyTorch.
-
-#### Enabling PyTorch
-
-PyTorch can be enabled in one of two ways. First, by adding `--torch` to the
-`mlagents-learn` command. This will make all behaviors train with PyTorch.
-
-Second, by changing the `framework` option for your agent behavior in the
-configuration YAML as below. This will use PyTorch just for that behavior.
-
-```yaml
-behaviors:
-  YourAgentBehavior:
-    framework: pytorch
-```
diff --git a/docs/Unity-Inference-Engine.md b/docs/Unity-Inference-Engine.md
@@ -29,9 +29,9 @@ There are currently two supported model formats:
   [industry-standard open format](https://onnx.ai/about.html) produced by the
   [tf2onnx package](https://github.com/onnx/tensorflow-onnx).
 
-Export to ONNX is currently considered beta. To enable it, make sure
-`tf2onnx>=1.5.5` is installed in pip. tf2onnx does not currently support
-tensorflow 2.0.0 or later, or earlier than 1.12.0.
+Export to ONNX is used if using PyTorch (the default). To enable it
+while using TensorFlow, make sure `tf2onnx>=1.5.5` is installed in pip.
+tf2onnx does not currently support tensorflow 2.0.0 or later, or earlier than 1.12.0.
 
 ## Using the Unity Inference Engine