Skip to content
master
Switch branches/tags
Go to file
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
Jan 28, 2020

README.md

Batch-Constrained Deep Q-Learning (BCQ)

Batch-Constrained deep Q-learning (BCQ) is the first batch deep reinforcement learning, an algorithm which aims to learn offline without interactions with the environment.

BCQ was first introduced in our ICML 2019 paper which focused on continuous action domains. A discrete-action version of BCQ was introduced in a followup Deep RL workshop NeurIPS 2019 paper. Code for each of these algorithms can be found under their corresponding folder.

Bibtex

@inproceedings{fujimoto2019off,
  title={Off-Policy Deep Reinforcement Learning without Exploration},
  author={Fujimoto, Scott and Meger, David and Precup, Doina},
  booktitle={International Conference on Machine Learning},
  pages={2052--2062},
  year={2019}
}
@article{fujimoto2019benchmarking,
  title={Benchmarking Batch Deep Reinforcement Learning Algorithms},
  author={Fujimoto, Scott and Conti, Edoardo and Ghavamzadeh, Mohammad and Pineau, Joelle},
  journal={arXiv preprint arXiv:1910.01708},
  year={2019}
}

About

Author's PyTorch implementation of BCQ for continuous and discrete actions

Resources

License

Releases

No releases published

Packages

No packages published

Languages