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

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The ML-Agents Toolkit provides several advanced features that extend the core functionality and enable sophisticated use cases.
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| **Feature** | **Description** |
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|------------------------------------------------------|----------------------------------------------------------|
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| [Custom Side Channels](Custom-SideChannels.md) | Create custom communication channels between Unity and Python. |
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| [Custom Grid Sensors](Custom-GridSensors.md) | Build specialized grid-based sensors for spatial data. |
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| [Input System Integration](InputSystem-Integration.md) | Integrate ML-Agents with Unity's Input System. |
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| [Inference Engine](Inference-Engine.md) | Deploy trained models for real-time inference. |
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| [Hugging Face Integration](Hugging-Face-Integration.md) | Connect with Hugging Face models and ecosystem. |
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| [ML-Agents Package Settings](Package-Settings.md) | Configure advanced package settings and preferences. |
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| [Unity Environment Registry](Unity-Environment-Registry.md) | Manage and register Unity environments programmatically. |
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| **Feature** | **Description** |
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|-------------------------------------------------------------|----------------------------------------------------------------|
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| [Custom Side Channels](Custom-SideChannels.md) | Create custom communication channels between Unity and Python. |
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| [Custom Grid Sensors](Custom-GridSensors.md) | Build specialized grid-based sensors for spatial data. |
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| [Input System Integration](InputSystem-Integration.md) | Integrate ML-Agents with Unity's Input System. |
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| [Inference Engine](Inference-Engine.md) | Deploy trained models for real-time inference. |
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| [Hugging Face Integration](Hugging-Face-Integration.md) | Connect with Hugging Face models and ecosystem. |
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| [ML-Agents Package Settings](Package-Settings.md) | Configure advanced package settings and preferences. |
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| [Unity Environment Registry](Unity-Environment-Registry.md) | Manage and register Unity environments programmatically. |

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

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This section provides foundational knowledge to help you understand the technologies and concepts that power the ML-Agents Toolkit.
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| **Topic** | **Description** |
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|----------------------------------------------------|-------------------------------------------------------------------------------|
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| [Machine Learning](Background-Machine-Learning.md) | Introduction to ML concepts, reinforcement learning, and training principles. |
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| [Unity](Background-Unity.md) | Unity fundamentals for ML-Agents development and environment creation. |
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| [PyTorch](Background-PyTorch.md) | PyTorch basics for understanding the training pipeline and neural networks. |
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| [Using Virtual Environment](Using-Virtual-Environment.md) | Setting up and managing Python virtual environments for ML-Agents. |
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| [ELO Rating System](ELO-Rating-System.md) | Understanding ELO rating system for multi-agent training evaluation. |
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| **Topic** | **Description** |
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|-----------------------------------------------------------|-------------------------------------------------------------------------------|
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| [Machine Learning](Background-Machine-Learning.md) | Introduction to ML concepts, reinforcement learning, and training principles. |
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| [Unity](Background-Unity.md) | Unity fundamentals for ML-Agents development and environment creation. |
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| [PyTorch](Background-PyTorch.md) | PyTorch basics for understanding the training pipeline and neural networks. |
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| [Using Virtual Environment](Using-Virtual-Environment.md) | Setting up and managing Python virtual environments for ML-Agents. |
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| [ELO Rating System](ELO-Rating-System.md) | Understanding ELO rating system for multi-agent training evaluation. |

‎com.unity.ml-agents/Documentation~/Cloud-Deployment.md‎

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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.
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| **Platform** | **Description** |
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|---------------------------------------------------|-----------------------------------------------------------|
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| [Using Docker](Using-Docker.md) | Containerized deployment with Docker (deprecated). |
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| [Amazon Web Services](Training-on-Amazon-Web-Service.md) | Training on AWS cloud infrastructure (deprecated). |
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| [Microsoft Azure](Training-on-Microsoft-Azure.md) | Training on Azure cloud services (deprecated). |
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| **Platform** | **Description** |
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|----------------------------------------------------------|------------------------------------------------------|
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| [Using Docker](Using-Docker.md) | Containerized deployment with Docker (deprecated). |
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| [Amazon Web Services](Training-on-Amazon-Web-Service.md) | Training on AWS cloud infrastructure (deprecated). |
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| [Microsoft Azure](Training-on-Microsoft-Azure.md) | Training on Azure cloud services (deprecated). |
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‎com.unity.ml-agents/Documentation~/Reference-Support.md‎

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The Reference & Support section contains essential documentation for ongoing ML-Agents development.
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| **Resource** | **Description** |
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|-----------------------------------|-------------------------------------------------------------------|
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| [FAQ](FAQ.md) | Frequently asked questions and common issues with solutions. |
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| [Limitations](Limitations.md) | Known limitations and constraints of the ML-Agents Toolkit. |
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| [Migrating](Migrating.md) | Migration guides for updating between ML-Agents versions. |
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| [Versioning](Versioning.md) | Information about ML-Agents versioning and release notes. |
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| [ML-Agents Glossary](Glossary.md) | Glossary of terms and concepts used in ML-Agents. |
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| [C# API Reference](API-Reference.md) | C# API documentation generated from code comments. |
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| **Resource** | **Description** |
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|--------------------------------------|--------------------------------------------------------------|
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| [FAQ](FAQ.md) | Frequently asked questions and common issues with solutions. |
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| [Limitations](Limitations.md) | Known limitations and constraints of the ML-Agents Toolkit. |
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| [Migrating](Migrating.md) | Migration guides for updating between ML-Agents versions. |
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| [Versioning](Versioning.md) | Information about ML-Agents versioning and release notes. |
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| [ML-Agents Glossary](Glossary.md) | Glossary of terms and concepts used in ML-Agents. |
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| [C# API Reference](API-Reference.md) | C# API documentation generated from code comments. |

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

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Use the following topics to learn about training in ML-Agents.
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| **Section** | **Description** |
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|---------------------------------------------------------------|----------------------------------------|
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| [Training ML-Agents Basics](Training-ML-Agents.md) | Overview of training workflow. |
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| [Training Configuration File](Training-Configuration-File.md) | Training config file reference. |
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| [Using Tensorboard](Using-Tensorboard.md) | Visualize training progress. |
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| [Customizing Training via Plugins](Training-Plugins.md) | Extend training with custom plugins. |
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| [Custom Trainer Plugin](Tutorial-Custom-Trainer-Plugin.md) | Tutorial for creating custom trainers. |
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| **Section** | **Description** |
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|---------------------------------------------------------------|--------------------------------------------|
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| [Training ML-Agents Basics](Training-ML-Agents.md) | Overview of training workflow. |
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| [Training Configuration File](Training-Configuration-File.md) | Training config file reference. |
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| [Using Tensorboard](Using-Tensorboard.md) | Visualize training progress. |
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| [Customizing Training via Plugins](Training-Plugins.md) | Extend training with custom plugins. |
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| [Custom Trainer Plugin](Tutorial-Custom-Trainer-Plugin.md) | Tutorial for creating custom trainers. |
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| [Profiling Trainers](Profiling-Python.md) | Analyze and optimize training performance. |

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

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| [ML-Agents Theory](ML-Agents-Overview.md) | Learn about core concepts of ML-Agents. |
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| [Get started](Get-Started.md) | Learn how to install ML-Agents and explore samples. |
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| [Learning Environments and Agents](Learning-Environments-Agents.md) | Learn about Environments, Agents, creating environments, and using executable builds. |
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| [Learning Environments and Agents](Learning-Environments-Agents.md) | Learn about Environments, Agents, creating environments, and using executable builds. |
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| [Training](Training.md) | Training workflow, config file, monitoring tools, custom plugins, and profiling. |
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| [Python APIs](Python-APIs.md) | Gym, PettingZoo, low-level interfaces, and trainer documentation. |
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| [Python Tutorial with Google Colab](Tutorial-Colab.md) | Interactive tutorials for using ML-Agents with Google Colab. |
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| [Advanced Features](Advanced-Features.md) | Custom sensors, side channels, package settings, environment registry, and input system integrations. |
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| [Cloud & Deployment](Cloud-Deployment.md) | Legacy cloud deployment guides (deprecated). |
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| [Reference & Support](Reference-Support.md) | FAQ, troubleshooting, migration guides, and C# API reference. |
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| [Background](Background.md) | Machine Learning, Unity, PyTorch fundamentals, virtual environments, and ELO rating systems. |
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| [Background](Background.md) | Machine Learning, Unity, PyTorch fundamentals, virtual environments, and ELO rating systems. |
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## Capabilities
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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:

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