Unity-Technologies/ml-agents

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 - Platform for training RL agents in games and simulations.
• 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.

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