Unity-Technologies/ml-agents
Ml Agents
This project is a reinforcement learning toolkit and simulation-based AI trainer for creating intelligent agents within Unity simulations. It provides a multi-agent simulation framework for configuring cooperative or competitive scenarios and includes an environment wrapper that bridges simulations with standard machine learning libraries using gym-style interfaces.
The system features a native cross-platform inference engine that executes trained neural network models for real-time decision making without external dependencies. It enables the acceleration of the learning process by running multiple identical environment copies in parallel to collect experience data.
The toolkit covers various training methodologies, including deep reinforcement learning, imitation learning from demonstrations, and curriculum-based task sequencing. It also supports environment randomization to prevent overfitting and improve generalization across different physical and visual simulation settings.
Features
- Reinforcement Learning Environments - Provides a comprehensive framework and simulation environment for training intelligent agents using reinforcement and imitation learning.
- Reinforcement Learning Training Utilities - Provides a complete toolkit for training intelligent agents using deep reinforcement learning within simulated environments.
- API Compatibility Layers - Provides a compatibility layer that translates simulation state and actions into standard ML framework formats.
- Cross-Platform Inference Frameworks - Provides a native inference engine to run trained neural networks across multiple platforms.
- Model Deployment - Integrates trained neural networks into production environments for real-time decision making.
- ML Environment Bridges - Bridges Unity simulations with standard machine learning libraries to facilitate efficient model training.
- Multi-Agent Frameworks - Offers a comprehensive toolkit for configuring complex cooperative and competitive multi-agent interactions.
- Multi-Agent Systems - Provides a framework for designing scenarios where multiple agents operate individually, cooperatively, or competitively.
- Simulation-Based Trainers - Provides a system that accelerates neural network training by collecting experience data across concurrent simulation instances.
- Parallel Experience Collection - Runs multiple identical environment copies in parallel to accelerate experience data collection.
- Simulator Wrappers - Provides a standardized wrapper for Unity simulations to enable robotic and agent policy training.
- Game Simulation Environments - Creates physics-based scenarios in Unity to serve as training grounds for machine learning agents.
- ML Interface Adapters - Adapts simulated environments into compatible interfaces for common machine learning libraries and gym-style APIs.
- Curriculum Learning Frameworks - Implements tools for scheduling increasingly difficult training tasks to guide agents toward complex behavior mastery.
- Expert Imitation Learning - Utilizes imitation learning algorithms to train agents based on recorded human or expert demonstrations.
- Inference Engines - Ships a native inference engine for executing neural network models in real-time without external dependencies.
- Parallel Simulation Environments - The system runs multiple copies of a simulation simultaneously to collect experience more quickly and accelerate the learning process.
- Domain Randomizations - Varies environment parameters to improve agent robustness and prevent overfitting across diverse scenarios.
- Curriculum Complexity Scaling - Implements a progression of increasing technical difficulty to guide agent learning.
- Reinforcement Learning - Toolkit for training intelligent agents within game development environments.
- Reinforcement Learning Environments - Toolkit for training intelligent agents in Unity-based environments.
- Research Platforms - Toolkit for creating reinforcement learning environments in Unity.
- Unified Toolkits - Platform for training intelligent agents within the Unity engine.
