BlankSHC / CQL-1

PyTorch implementation of the Offline Reinforcement Learning algorithm CQL. Includes the versions DQN-CQL and SAC-CQL for discrete and continuous action spaces.

Conservative Q-Learning (CQL)

PyTorch implementation of the CQL algorithm (Paper). Including the discrete action space DQN-CQL version, the continuous action space SAC-CQL version and a discrete CQL-SAC implementation.

Setup

-> conda environment [ ] -> requirement.txt [ ]

Run

Select the folder [CQL-DQN, CQL-SAC, CQL-SAC-discrete] of the algorithm you want to train and run: python train.py

Online RL Results:

Base CQL-DQN

CQL-SAC

CQL-SAC-discrete

Comparison of a discrete CQL-SAC implementations vs the normal discrete SAC.

CartPole

LunarLander

Offline RL Results:

Results

Find all training results and hyperparameter in the wandb project.

TODO:

• update readme [ ]
• add distributional Q-Function [ ]

Help and issues:

Im open for feedback, found bugs, improvements or anything. Just leave me a message or contact me.

Author

• Sebastian Dittert

Feel free to use this code for your own projects or research.

@misc{SAC,
  author = {Dittert, Sebastian},
  title = {CQL},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/BY571/CQL}},
}