Update more sections

maryamziaa · maryamziaa · commit de0d8deb97a4 · 2025-08-04T14:30:08.000-04:00
diff --git a/com.unity.ml-agents/Documentation~/Background-Machine-Learning.md b/com.unity.ml-agents/Documentation~/Background-Machine-Learning.md
@@ -113,13 +113,11 @@ good policies can be difficult (and/or time-consuming) for complex environments.
 
 <div style="text-align: center"><img src="images/rl_cycle.png" alt="The reinforcement learning lifecycle."></div>
 
-[Learning a policy](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/)
-usually requires many trials and iterative policy updates. More specifically,
+Learning a policy usually requires many trials and iterative policy updates. More specifically,
 the robot is placed in several fire situations and over time learns an optimal
 policy which allows it to put out fires more effectively. Obviously, we cannot
 expect to train a robot repeatedly in the real world, particularly when fires
-are involved. This is precisely why the use of
-[Unity as a simulator](https://blogs.unity3d.com/2018/01/23/designing-safer-cities-through-simulations/)
+are involved. This is precisely why the use of Unity as a simulator
 serves as the perfect training grounds for learning such behaviors. While our
 discussion of reinforcement learning has centered around robots, there are
 strong parallels between robots and characters in a game. In fact, in many ways,
diff --git a/com.unity.ml-agents/Documentation~/Background.md b/com.unity.ml-agents/Documentation~/Background.md
@@ -0,0 +1,9 @@
+# Background
+
+This section provides foundational knowledge to help you understand the technologies and concepts that power the ML-Agents Toolkit. 
+
+| **Topic**                                          | **Description**                                                               |
+|----------------------------------------------------|-------------------------------------------------------------------------------|
+| [Machine Learning](Background-Machine-Learning.md) | Introduction to ML concepts, reinforcement learning, and training principles. |
+| [Unity](Background-Unity.md)                       | Unity fundamentals for ML-Agents development and environment creation.        |
+| [PyTorch](Background-PyTorch.md)                   | PyTorch basics for understanding the training pipeline and neural networks.   |
diff --git a/com.unity.ml-agents/Documentation~/Cloud-Deployment.md b/com.unity.ml-agents/Documentation~/Cloud-Deployment.md
@@ -0,0 +1,12 @@
+# Cloud & Deployment (deprecated)
+
+
+This section contains legacy documentation for deploying ML-Agents training in cloud environments. While these approaches may still work, they are no longer actively maintained or recommended.
+
+
+| **Platform**                                      | **Description**                                           |
+|---------------------------------------------------|-----------------------------------------------------------|
+| [Using Docker](Using-Docker.md)                  | Containerized deployment with Docker (deprecated).       |
+| [Amazon Web Services](Training-on-Amazon-Web-Service.md) | Training on AWS cloud infrastructure (deprecated). |
+| [Microsoft Azure](Training-on-Microsoft-Azure.md) | Training on Azure cloud services (deprecated).          |
+
diff --git a/com.unity.ml-agents/Documentation~/Limitations.md b/com.unity.ml-agents/Documentation~/Limitations.md
@@ -2,6 +2,6 @@
 
 See the package-specific Limitations pages:
 
-- [`com.unity.mlagents` Unity package](https://docs.unity3d.com/Packages/com.unity.ml-agents@2.3/manual/index.html#known-limitations)
-- [`mlagents` Python package](../ml-agents/README.md#limitations)
-- [`mlagents_envs` Python package](../ml-agents-envs/README.md#limitations)
+- [`com.unity.mlagents` Unity package](Package-Limitations.md)
+- [`mlagents` Python package](../../ml-agents/README.md#limitations)
+- [`mlagents_envs` Python package](../../ml-agents-envs/README.md#limitations)
diff --git a/com.unity.ml-agents/Documentation~/ML-Agents-Overview.md b/com.unity.ml-agents/Documentation~/ML-Agents-Overview.md
diff --git a/com.unity.ml-agents/Documentation~/Package-Limitations.md b/com.unity.ml-agents/Documentation~/Package-Limitations.md
@@ -0,0 +1,20 @@
+## Package Limitations
+### Training
+Training is limited to the Unity Editor and Standalone builds on Windows, MacOS, and Linux with the Mono scripting backend. Currently, training does not work with the IL2CPP scripting backend. Your environment will default to inference mode if training is not supported or is not currently running.
+
+### Inference
+Inference is executed via Unity Inference Engine on the end-user device. Therefore, it is subject to the performance limitations of the end-user CPU or GPU. Also, only models created with our trainers are supported for running ML-Agents with a neural network behavior.
+
+### Headless Mode
+If you enable Headless mode, you will not be able to collect visual observations from your agents.
+
+### Rendering Speed and Synchronization
+Currently the speed of the game physics can only be increased to 100x real-time. The Academy (the sentinel that controls the stepping of the game to make sure everything is synchronized, from collection of observations to applying actions generated from policy inference to the agent) also moves in time with `FixedUpdate()` rather than `Update()`, so game behavior implemented in `Update()` may be out of sync with the agent decision-making. See [Execution Order of Event Functions](https://docs.unity3d.com/Manual/execution-order.html) for more information.
+
+You can control the frequency of Academy stepping by calling `Academy.Instance.DisableAutomaticStepping()`, and then calling `Academy.Instance.EnvironmentStep()`.
+
+### Input System Integration
+
+For `InputActuatorComponent` (see [Input System Integration](InputSystem-Integration.md) for more information): 
+- Limited implementation of `InputControls`
+- No way to customize the action space of the `InputActuatorComponent`
diff --git a/com.unity.ml-agents/Documentation~/Reference-Support.md b/com.unity.ml-agents/Documentation~/Reference-Support.md
@@ -0,0 +1,10 @@
+# Reference & Support
+
+The Reference & Support section contains essential documentation for ongoing ML-Agents development.
+
+
+| **Resource**                    | **Description**                                                   |
+|---------------------------------|-------------------------------------------------------------------|
+| [FAQ](FAQ.md)                   | Frequently asked questions and common issues with solutions.      |
+| [Limitations](Limitations.md)   | Known limitations and constraints of the ML-Agents Toolkit.      |
+| [Migrating](Migrating.md)       | Migration guides for updating between ML-Agents versions.        |
diff --git a/com.unity.ml-agents/Documentation~/TableOfContents.md b/com.unity.ml-agents/Documentation~/TableOfContents.md
@@ -16,42 +16,50 @@
   * [Python Gym API](Python-Gym-API.md)
   * [Python PettingZoo API](Python-PettingZoo-API.md)
   * [Python Low-Level API](Python-LLAPI.md)
+* [Python Tutorial with Google Colab]
+  * [Using a UnityEnvironment]
+  * [Q-Learning with a UnityEnvironment]
+  * [Using Side Channels on a UnityEnvironment]
 * [Advanced Features](Advanced-Features.md)
   * [Custom Side Channels](Custom-SideChannels.md)
   * [Custom Grid Sensors](Custom-GridSensors.md)
   * [Input System Integration](InputSystem-Integration.md)
   * [Inference Engine](Inference-Engine.md)
   * [Hugging Face Integration](Hugging-Face-Integration.md)
-* [Cloud & Deployment (deprecated)]()
+* [Cloud & Deployment (deprecated)](Cloud-Deployment.md)
   * [Using Docker](Using-Docker.md)
   * [Amazon Web Services](Training-on-Amazon-Web-Service.md)
   * [Microsoft Azure](Training-on-Microsoft-Azure.md)
-* [Reference & Support]()
+* [Reference & Support](Reference-Support.md)
   * [FAQ](FAQ.md)
   * [Limitations](Limitations.md)
   * [Migrating](Migrating.md)
-  * [Background: Machine Learning](Background-Machine-Learning.md)
-  * [Background: Unity](Background-Unity.md)
-  * [Background: PyTorch](Background-PyTorch.md)
+  * [versioning](Versioning.md)
+  * [ML-Agents Glossary](Glossary.md)
+* [Background](Background.md)
+  * [Machine Learning](Background-Machine-Learning.md)
+  * [Unity](Background-Unity.md)
+  * [PyTorch](Background-PyTorch.md)
+  * [ELO](ELO-Rating-System.md)
 
 ## Next Steps
 
 
 multienv vs multi instances
-colab
+Using an Executable Environment
+ML-Agents Package Settings
+Profiling Trainers
+How to use the Unity Environment Registry
+Python API Documentation
+Python-On-Off-Policy-Trainer-Documentation
+Custom Trainer Plugin
+Customizing Training via Plugins
 
 
 [the Agent documentation](Learning-Environment-Design-Agents.md#decisions)
 Hyperparameters are explained in [the training configuration file documentation](Training-Configuration-File.md)
 
-## Help
 
-If you run into any problems regarding ML-Agents, refer to our [FAQ](FAQ.md) and
-our [Limitations](Limitations.md) pages. If you can't find anything please
-[submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) and
-make sure to cite relevant information on OS, Python version, and exact error
-message (whenever possible).
-package
 ## Capabilities
 The package allows you to convert any Unity scene into a learning environment and train character behaviors using a variety of machine-learning algorithms. Additionally, it allows you to embed these trained behaviors back into Unity scenes to control your characters. More specifically, the package provides the following core functionalities:
 
diff --git a/com.unity.ml-agents/Documentation~/index.md b/com.unity.ml-agents/Documentation~/index.md
@@ -11,13 +11,15 @@ Note that the C# package does not contain the machine learning algorithms for tr
 
 | **Section**                                                         | **Description**                                                      |
 |---------------------------------------------------------------------|----------------------------------------------------------------------|
-| [ML-Agents Theory](ML-Agents-Theory.md)                             | Learn about core concepts of ML-Agents.                              |
+| [ML-Agents Theory](ML-Agents-Overview.md)                           | Learn about core concepts of ML-Agents.                              |
 | [Get started](Get-Started.md)                                       | Learn how to install ML-Agents and explore samples.                  |
 | [Learning Environments and Agents](Learning-Environments-Agents.md) | Learn about Environments, Agents, and creating your own Environment. |
 | [Training](Training.md)                                             | Training workflow, config file, and monitoring tools.                |
 | [Python APIs](Python-APIs.md)                                       | Gym, PettingZoo, and low-level Python interfaces.                    |
 | [Advanced Features](Advanced-Features.md)                           | Custom sensors, side channels, and specialized integrations.         |
-
+| [Cloud & Deployment](Cloud-Deployment.md)                           | Legacy cloud deployment guides (deprecated).                         |
+| [Background](Background.md)                                         | Machine Learning, Unity, and PyTorch fundamentals.                   |
+| [Reference & Support](Reference-Support.md)                         | FAQ, troubleshooting, migration guides, and background materials.    |
 
 ## Community and Feedback (review needed)
 
