A Minimalist Approach to Offline Reinforcement Learning

This is a clone of the original TD3BC repo.

TD3+BC is a simple approach to offline RL where only two changes are made to TD3: (1) a weighted behavior cloning loss is added to the policy update and (2) the states are normalized. Unlike competing methods there are no changes to architecture or underlying hyperparameters. The paper can be found here.

Usage

Paper results were collected with MuJoCo 1.50 (and mujoco-py 1.50.1.1) in OpenAI gym 0.17.0 with the D4RL datasets. Networks are trained using PyTorch 1.4.0 and Python 3.6.

The paper results can be reproduced by running:

./run_experiments.sh

Bibtex

@inproceedings{fujimoto2021minimalist,
	title={A Minimalist Approach to Offline Reinforcement Learning},
	author={Scott Fujimoto and Shixiang Shane Gu},
	booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
	year={2021},
}

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*This is not an official Google product.