Unity ML-Agents (Machine Learning Agents) is an open-source toolkit developed by Unity Technologies that enables researchers and developers to train and evaluate AI agents in realistic 3D environments. Built on the Unity game engine, it bridges the gap between game simulation and machine learning by allowing AI models to interact with and learn from virtual worlds in real time.

Designed for machine learning practitioners, robotics researchers, and game developers, ML-Agents supports reinforcement learning, imitation learning, self-play, and curriculum learning—all within customizable environments.

Key benefits of Unity ML-Agents include:

• Flexible and scalable simulations in complex 3D settings

• Out-of-the-box support for RL algorithms using Python-based APIs

• Integration with Unity's powerful physics and rendering engine for high-fidelity learning scenarios

What are the main features of Unity ML-Agents?

Real-time 3D simulation with Unity Engine

ML-Agents leverages Unity’s game engine to create rich, interactive environments where agents can learn from visual, physical, and auditory feedback.

• Realistic lighting, textures, and motion

• Custom physics behaviors via Unity’s PhysX engine

• Simulation of multi-agent and multi-object environments

Python API and OpenAI Gym compatibility

The toolkit includes a Python API that enables easy connection between Unity environments and external training libraries.

• Compatible with PyTorch, TensorFlow, and other ML frameworks

• Exports environments as Gym-compatible interfaces

• Enables quick experimentation with custom learning pipelines

Multiple learning paradigms

Unity ML-Agents supports various machine learning approaches:

• Reinforcement learning via Proximal Policy Optimization (PPO)

• Imitation learning through behavior cloning

• Self-play for adversarial training

• Curriculum learning to gradually increase task difficulty

Flexible agent and environment design

Users can design custom agents, sensors, and rewards within the Unity Editor, allowing highly tailored training scenarios.

• Control over observation types (visual, vector, raycast, etc.)

• Configurable reward structures and episode parameters

• Easy integration of domain-specific rules and constraints

Built-in training and visualization tools

ML-Agents includes command-line tools and visual dashboards to monitor training progress and agent performance.

• TensorBoard integration for performance tracking

• Built-in training loop via the mlagents-learn CLI

• Support for model exporting and inference inside Unity

Why choose Unity ML-Agents?

• Combines high-fidelity simulation with machine learning, ideal for embodied AI research

• Supports diverse training strategies, including reinforcement and imitation learning

• Open-source and actively developed, with contributions from both Unity and the AI research community

• Highly customizable training environments, from simple mazes to full 3D games

• Cross-platform and visually rich, helping bridge simulation and real-world deployment