Update Python APIs and Advanced Features

Author: maryamziaa

com.unity.ml-agents/Documentation~/Advanced-Features.md

@@ -0,0 +1,18 @@
+# Advanced Features
+
+The ML-Agents Toolkit provides several advanced features that extend the core functionality and enable sophisticated use cases.
+These advanced features are particularly useful when:
+
+- You need custom data exchange between Unity and Python environments
+- Your agents require specialized sensor types for spatial or grid-based observations
+- You want to integrate user input with trained agent behaviors
+- You're deploying trained models in production environments
+- You want to leverage pre-trained models from the Hugging Face ecosystem
+
+| **Feature**                                                   | **Description**                                           |
+|---------------------------------------------------------------|-----------------------------------------------------------|
+| [Custom Side Channels](Custom-SideChannels.md)               | Create custom communication channels between Unity and Python. |
+| [Custom Grid Sensors](Custom-GridSensors.md)                 | Build specialized grid-based sensors for spatial data.   |
+| [Input System Integration](InputSystem-Integration.md)       | Integrate ML-Agents with Unity's Input System.           |
+| [Inference Engine](Inference-Engine.md)                      | Deploy trained models for real-time inference.           |
+| [Hugging Face Integration](Hugging-Face-Integration.md)      | Connect with Hugging Face models and ecosystem.          |

com.unity.ml-agents/Documentation~/Python-APIs.md

@@ -0,0 +1,10 @@
+# Python APIs
+
+The Python APIs allow you to control and interact with Unity environments from Python scripts. Each API is designed for specific use cases and offers different levels of abstraction and functionality.
+
+
+| **API**                                           | **Description**                                                    |
+|---------------------------------------------------|--------------------------------------------------------------------|
+| [Python Gym API](Python-Gym-API.md)              | OpenAI Gym-compatible interface for standard RL workflows.        |
+| [Python PettingZoo API](Python-PettingZoo-API.md)| Multi-agent environment interface compatible with PettingZoo.     |
+| [Python Low-Level API](Python-LLAPI.md)          | Direct low-level access for custom training and advanced use cases.|

com.unity.ml-agents/Documentation~/Python-Gym-API.md

@@ -12,7 +12,7 @@ Unity environment via Python.
 ## Installation
 
 The gym wrapper is part of the `mlagents_envs` package. Please refer to the
-[mlagents_envs installation instructions](ML-Agents-Envs-README.md).
+[mlagents_envs installation instructions](../../ml-agents-envs/README.md).
 
 
 ## Using the Gym Wrapper
@@ -87,7 +87,7 @@ pip install git+git://github.com/openai/baselines
 Next, create a file called `train_unity.py`. Then create an `/envs/` directory
 and build the environment to that directory. For more information on
 building Unity environments, see
-[here](../docs/Learning-Environment-Executable.md). Note that because of
+[here](Learning-Environment-Executable.md). Note that because of
 limitations of the DQN baseline, the environment must have a single visual
 observation, a single discrete action and a single Agent in the scene.
 Add the following code to the `train_unity.py` file:
@@ -253,7 +253,7 @@ method with the following code.
 
 `<path-to-environment>` is the path to your built Unity executable. For more
 information on building Unity environments, see
-[here](../docs/Learning-Environment-Executable.md), and note the Limitations
+[here](Learning-Environment-Executable.md), and note the Limitations
 section below.
 
 Note that we are not using the preprocessor from Dopamine, as it uses many

com.unity.ml-agents/Documentation~/Python-LLAPI.md

@@ -39,7 +39,7 @@ The key objects in the Python API include:
   DecisionSteps and TerminalSteps as well as the expected action shapes.
 
 These classes are all defined in the
-[base_env](../ml-agents-envs/mlagents_envs/base_env.py) script.
+[base_env](../../ml-agents-envs/mlagents_envs/base_env.py) script.
 
 An Agent "Behavior" is a group of Agents identified by a `BehaviorName` that
 share the same observations and action types (described in their

com.unity.ml-agents/Documentation~/Python-PettingZoo-API.md

@@ -8,7 +8,7 @@ interfacing with a Unity environment via Python.
 ## Installation and Examples
 
 The PettingZoo wrapper is part of the `mlagents_envs` package. Please refer to the
-[mlagents_envs installation instructions](ML-Agents-Envs-README.md).
+[mlagents_envs installation instructions](../../ml-agents-envs/README.md).
 
 [[Colab] PettingZoo Wrapper Example](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/develop-python-api-ga/ml-agents-envs/colabs/Colab_PettingZoo.ipynb)
 

com.unity.ml-agents/Documentation~/TableOfContents.md

@@ -12,16 +12,16 @@
   * [Training ML-Agents Basics](Training-ML-Agents.md)
   * [Training Configuration File](Training-Configuration-File.md)
   * [Using Tensorboard](Using-Tensorboard.md)
-* [Python APIs]()
+* [Python APIs](Python-APIs.md)
   * [Python Gym API](Python-Gym-API.md)
   * [Python PettingZoo API](Python-PettingZoo-API.md)
   * [Python Low-Level API](Python-LLAPI.md)
-* [Advanced Features]()
+* [Advanced Features](Advanced-Features.md)
   * [Custom Side Channels](Custom-SideChannels.md)
-  * [Inference Engine](Inference-Engine.md)
-  * [Hugging Face Integration](Hugging-Face-Integration.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)]()
   * [Using Docker](Using-Docker.md)
   * [Amazon Web Services](Training-on-Amazon-Web-Service.md)
@@ -36,14 +36,10 @@
 
 ## Next Steps
 
-| [Making a New Learning Environment](Learning-Environment-Create-New.md) | Create your own Learning Environment.   |
 
-- For more information on the ML-Agents Toolkit, in addition to helpful
-  background, check out the [ML-Agents Toolkit Overview](ML-Agents-Overview.md)
-  page.
+multienv vs multi instances
+colab
 
-- For more information on the various training options available, check out the
-  [Training ML-Agents](Training-ML-Agents.md) page.
 
 [the Agent documentation](Learning-Environment-Design-Agents.md#decisions)
 Hyperparameters are explained in [the training configuration file documentation](Training-Configuration-File.md)
@@ -74,30 +70,8 @@ great resource as they provide sample usage of almost all of our features.
 
 ### * [Training Plugins](Training-Plugins.md)
 
-### For a broad overview of reinforcement learning, imitation learning and all the
-training scenarios, methods and options within the ML-Agents Toolkit, see
-[ML-Agents Toolkit Overview](ML-Agents-Overview.md).
-
-[Inference Engine](Inference-Engine.md)
 
 
-## Summary and Next Steps
 
-To briefly summarize: The ML-Agents Toolkit enables games and simulations built
-in Unity to serve as the platform for training intelligent agents. It is
-designed to enable a large variety of training modes and scenarios and comes
-packed with several features to enable researchers and developers to leverage
-(and enhance) machine learning within Unity.
 
-In terms of next steps:
 
-- For a walkthrough of running ML-Agents with a simple scene, check out the
-  [Getting Started](Sample.md) guide.
-- For a "Hello World" introduction to creating your own Learning Environment,
-  check out the
-  [Making a New Learning Environment](Learning-Environment-Create-New.md) page.
-- For an overview on the more complex example environments that are provided in
-  this toolkit, check out the
-  [Example Environments](Learning-Environment-Examples.md) page.
-- For more information on the various training options available, check out the
-  [Training ML-Agents](Training-ML-Agents.md) page.

com.unity.ml-agents/Documentation~/index.md

@@ -15,6 +15,8 @@ Note that the C# package does not contain the machine learning algorithms for tr
 | [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.         |
 
 
 ## Community and Feedback (review needed)
@@ -58,4 +60,5 @@ Please refer to "Information that is passively collected by Unity" in the
 * [ML-Agents tutorials by CodeMonkeyUnity](https://www.youtube.com/playlist?list=PLzDRvYVwl53vehwiN_odYJkPBzcqFw110)
 * [Introduction to ML-Agents by Huggingface](https://huggingface.co/learn/deep-rl-course/en/unit5/introduction)
 * [Community created ML-Agents projects](https://discussions.unity.com/t/post-your-ml-agents-project/816756)
+* [ML-Agents models on Huggingface](https://huggingface.co/models?library=ml-agents)
 * [Blog posts](Blog-posts.md)