codecmd / ai-optimizer

The next generation deep reinforcement learning framework

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.