Project Title

ml-agents — Unity Machine Learning Agents Toolkit for Training Intelligent Agents

Overview

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning. It provides implementations of state-of-the-art algorithms, allowing game developers and hobbyists to train intelligent agents for 2D, 3D, and VR/AR games. Researchers can also use the Python API to train agents using various methods.

Key Features

17+ example Unity environments
Support for multiple environment configurations and training scenarios
Flexible Unity SDK for integration into games or custom scenes
Training support for single-agent, multi-agent cooperative, and competitive scenarios
Learning from demonstrations through Imitation Learning algorithms
Custom training algorithm and component integration
Curriculum Learning scenarios for complex tasks
Environment randomization for robust agent training
On Demand Decision Making for flexible agent control
Concurrent Unity environment instance training
Cross-platform support through the Inference Engine
Unity environment control from Python
Gym and PettingZoo environment wrapping

Use Cases

Training NPCs for various game settings, including multi-agent and adversarial scenarios
Automated testing of game builds
Evaluating game design decisions pre-release
Advancing AI research by evaluating AI on Unity's rich environments

Advantages

Enables training of intelligent agents in game and simulation environments
Provides a central platform for AI advances to be evaluated and shared
Supports a wide range of training methods and scenarios
Offers extensive documentation and example environments

Limitations / Considerations

The toolkit is primarily designed for Unity environments
May require significant setup and configuration for custom scenarios
Performance may vary depending on the complexity of the environment and the number of agents

TensorFlow Agents: A library for reinforcement learning in TensorFlow, offering a different set of algorithms and a focus on TensorFlow rather than Unity.
DeepMind Lab: A 3D game-like platform for developing and testing reinforcement learning agents, with a focus on research and a different set of environments.
Gym: A toolkit for developing and comparing reinforcement learning algorithms, providing a standardized API and a variety of environments but not specifically tailored for Unity.

Basic Information

GitHub: https://github.com/Unity-Technologies/ml-agents
Stars: 18,550
License: Apache 2.0
Last Commit: 2025-08-20

📊 Project Information

Project Name: ml-agents
GitHub URL: https://github.com/Unity-Technologies/ml-agents
Programming Language: C#
⭐ Stars: 18,550
🍴 Forks: 4,328
📅 Created: 2017-09-08
🔄 Last Updated: 2025-08-20

🏷️ Project Topics

Topics: [, ", d, e, e, p, -, l, e, a, r, n, i, n, g, ", ,, , ", d, e, e, p, -, r, e, i, n, f, o, r, c, e, m, e, n, t, -, l, e, a, r, n, i, n, g, ", ,, , ", m, a, c, h, i, n, e, -, l, e, a, r, n, i, n, g, ", ,, , ", n, e, u, r, a, l, -, n, e, t, w, o, r, k, s, ", ,, , ", r, e, i, n, f, o, r, c, e, m, e, n, t, -, l, e, a, r, n, i, n, g, ", ,, , ", u, n, i, t, y, ", ,, , ", u, n, i, t, y, 3, d, ", ]

📚 Documentation

This article is automatically generated by AI based on GitHub project information and README content analysis

ml-agents

Project Description