This repo is mostly designed based on Unity ML-Agents. Please first make sure you at least already know how to make a learning environment in ML-Agens.
For ML-Agents or related machine learning knowledge, see ML-Agents documentation.
- Getting Started with the 3D Balance Ball Environment
- Features and Basic Concepts
- Example Environments
- Training with Proximal Policy Optimization(PPO)
- Training with Imitation(Supervised Learning)
- Use Neural Evolution to optimize Neural Network
- Training a Generative Adversarial Network(GAN)
- Define Your Own Training Process for ML-Agent - See the source codes of Trainer.cs for details.