AI-Optimizer
AI-Optimizer is a next generation deep reinforcement learning suit, privoding rich algorithm libraries ranging from model-free to model-based RL algorithms, from single-agent to multi-agent algorithms. Moreover, AI-Optimizer contains a flexible and easy-to-use distributed training framekwork for efficient policy training.
-For now, AI-Optimizer privodes following built-in libraries and more libraries and implementations are comming soon.
- Multiagent Reinforcement learning
- Representation Reinforcement Learning
- Offline Reinforcement Learning
- Transfer Reinforcement Learning
- Model-based reinforcement learning
Repo: Multiagent Reinforcement Learning (MARL)
MARL repo contains the released codes of representative research works of TJU-RL-Lab on the topic of Multiagent Reinforcement Learning (MARL). The research topics are classified according to the critical challenges of MARL, e.g., the curse of dimensionality (scalability) issue, non-stationarity, multiagent credit assignment, exploration–exploitation tradeoff and hybrid action. see more here
Contributing
AI-Optimizer is still under development. More algorithms and features are going to be added and we always welcome contributions to help make AI-Optimizer better. Feel free to contribute.
