- ML-Agents Toolkit Overview
- Getting Started with the 3D Balance Ball Environment
- Example Environments
- Making a New Learning Environment
- Designing a Learning Environment
- Designing Agents
- Learning Environment Best Practices
- Using the Monitor
- Using the Video Recorder
- Using an Executable Environment
- Creating Custom Side Channels
- Training ML-Agents
- Using TensorBoard to Observe Training
- Training Using Concurrent Unity Instances
- Training with Proximal Policy Optimization
- Training with Soft Actor-Critic
- Training with Curriculum Learning
- Training with Imitation Learning
- Training with LSTM
- Training Generalized Reinforcement Learning Agents
- Migrating from earlier versions of ML-Agents
- Frequently Asked Questions
- ML-Agents Glossary
- Limitations
- API Reference
- How to use the Python API
- Wrapping Learning Environment as a Gym (+Baselines/Dopamine Integration)
To make the Unity ML-Agents toolkit accessible to the global research and Unity developer communities, we're attempting to create and maintain translations of our documentation. We've started with translating a subset of the documentation to one language (Chinese), but we hope to continue translating more pages and to other languages. Consequently, we welcome any enhancements and improvements from the community.
We no longer use them ourselves and so they may not be up-to-date. We've decided to keep them up just in case they are helpful to you.