- ML-Agents Toolkit Overview
- Getting Started with the 3D Balance Ball Environment
- Example Environments
- Making a New Learning Environment
- Designing a Learning Environment
- Learning Environment Best Practices
- 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)
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.