Updated docs. (#5986)

Author: miguelalonsojr

docs/Installation.md

@@ -42,7 +42,7 @@ Consequently, to install and use the ML-Agents Toolkit you will need to:
 strongly recommend that you install Unity through the Unity Hub as it will
 enable you to manage multiple Unity versions.
 
-### Install **Python 3.7.2** or Higher
+### Install **Python 3.10.12** or Higher
 
 We recommend [installing](https://www.python.org/downloads/) Python 3.7.
 If you are using Windows, please install the x86-64 version and not x86.
@@ -139,7 +139,7 @@ On Windows, you'll have to install the PyTorch package separately prior to
 installing ML-Agents. Activate your virtual environment and run from the command line:
 
 ```sh
-pip3 install torch~=1.7.1 -f https://download.pytorch.org/whl/torch_stable.html
+pip3 install torch~=1.13.1 -f https://download.pytorch.org/whl/torch_stable.html
 ```
 
 Note that on Windows, you may also need Microsoft's

docs/Integrations-Match3.md

@@ -102,8 +102,3 @@ The indexing for actions is the same as described in
 (for example, Figure 2b). The horizontal moves are enumerated first, then the vertical ones.
 <img src="images/match3-moves.png" align="center"/>
 
-## Feedback
-If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario,
-[we want to hear from you](https://forms.gle/TBsB9jc8WshgzViU9). Additionally, we are also looking for interested
-Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the
-[form](https://forms.gle/TBsB9jc8WshgzViU9). If selected, we will provide gift cards as a token of appreciation.

docs/Migrating.md

@@ -4,7 +4,7 @@
 <!---
 TODO: update ml-agents-env package version before release
 --->
-## Migrating to the ml-agents-envs 0.29.0 package
+## Migrating to the ml-agents-envs 0.30.0 package
 - Python 3.10 is now the minimum version of python supported due to [python3.6 EOL](https://endoflife.date/python).
   Please update your python installation to 3.10.12 or higher.
 - The `gym-unity` package has been refactored into the `ml-agents-envs` package. Please update your imports accordingly.
@@ -19,7 +19,7 @@ from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper
 ```
 
 
-## Migrating the package to version 2.0
+## Migrating the package to version 2.x
 - The official version of Unity ML-Agents supports is now 2022.3 LTS. If you run
   into issues, please consider deleting your project's Library folder and reponening your
   project.

docs/Readme.md

@@ -164,15 +164,15 @@ We have also published a series of blog posts that are relevant for ML-Agents:
 
 ### More from Unity
 
-- [Unity Robotics](https://github.com/Unity-Technologies/Unity-Robotics-Hub)
-- [Unity Computer Vision](https://github.com/Unity-Technologies/com.unity.perception)
+- [Unity Sentis](https://unity.com/products/sentis)
+- [Introductin Unity Muse and Sentis](https://blog.unity.com/engine-platform/introducing-unity-muse-and-unity-sentis-ai)
 
 ## Community and Feedback
 
 The ML-Agents Toolkit is an open-source project and we encourage and welcome
 contributions. If you wish to contribute, be sure to review our
 [contribution guidelines](CONTRIBUTING.md) and
-[code of conduct](../CODE_OF_CONDUCT.md).
+[code of conduct](CODE_OF_CONDUCT.md).
 
 For problems with the installation and setup of the ML-Agents Toolkit, or
 discussions about how to best setup or train your agents, please create a new

docs/Tutorial-Custom-Trainer-Plugin.md

@@ -1,3 +1,7 @@
+# Custom Trainer Plugin
+
+## How to write a custom trainer plugin
+
 ### Step 1: Write your custom trainer class
 Before you start writing your code, make sure to use your favorite environment management tool(e.g. `venv` or `conda`) to create and activate a Python virtual environment. The following command uses `conda`, but other tools work similarly:
 ```shell

docs/Unity-Inference-Engine.md

@@ -10,7 +10,7 @@ run the neural network within Unity.
 ## Supported devices
 
 See the Unity Inference Engine documentation for a list of the
-[supported platforms](https://docs.unity3d.com/Packages/com.unity.sentis@latest/index.html#supported-platforms).
+[supported platforms](https://docs.unity3d.com/Manual/PlatformSpecific.html).
 
 Scripting Backends : The Unity Inference Engine is generally faster with
 **IL2CPP** than with **Mono** for Standalone builds. In the Editor, It is not

docs/Using-Virtual-Environment.md

@@ -21,6 +21,10 @@ from dependencies of other projects. This has a few advantages:
 This guide has been tested with Python 3.10.12. Newer versions might not
 have support for the dependent libraries, so are not recommended.
 
+## Use Conda (or Mamba)
+
+While there are many options for setting up virtual environments for python, by far the most
+
 ## Installing Pip (Required)
 
 1. Download the `get-pip.py` file using the command

mkdocs.yml

@@ -13,6 +13,7 @@ nav:
     - Machine Learning: Background-Machine-Learning.md
     - PyTorch: Background-PyTorch.md
     - Unity: Background-Unity.md
+    - ELO: ELO-Rating-System.md
 - Interfacing with Unity Builds:
     - Getting started with the Gym API: Python-Gym-API.md
     - Getting started with the PettingZoo API: Python-PettingZoo-API.md
@@ -23,7 +24,9 @@ nav:
     - LLAPI Documentation: Python-LLAPI-Documentation.md
     - On/Off Policy Trainer: Python-On-Off-Policy-Trainer-Documentation.md
 - Tutorials:
+    - Customizing Training via Plugins: Training-Plugins.md
     - Custom Trainer Plugin: Tutorial-Custom-Trainer-Plugin.md
+    - HuggingFace: Hugging-Face-Integration.md
 - About:
     - FAQs: FAQ.md
     - Limitations: Limitations.md