Awesome

A Policy-Guided Imitation Approach for Offline Reinforcement Learning

This is the code for the paper A Policy-Guided Imitation Approach for Offline Reinforcement Learning accepted as oral at NeurIPS'2022. The paper and slide can be found at paper and slide.

Policy-guided Offline RL (POR) is a new offline RL paradigm, it enables state-stitching from the dataset rather than action-stitching as conducted in prior offline RL methods. POR enjoys training stability by using in-sample learning while still allowing logical out-of-sample generalization. We hope that POR could shed light on how to enable state-stitching in offline RL, which connects well to goal-conditioned RL and hierarchical RL.

Note that the copyright of our original code belongs to the previous company I worked for and the current github version is our re-implementation. Although we try to reimplement it as similar to the original version as possible, there exists some minor mismatch from the paper results (except for the "hopper-medium-v2" datasest, where our re-implementation version achieves a 78.6 score, not able to reproduce 98.2 in the white paper).

Usage

Mujoco reuslts can be reproduced by first running ./pretrain.sh and then running ./run_mujoco.sh, Antmaze results can be reproduced by running ./run_antmaze.sh. See our running results here.

Bibtex

@inproceedings{xu2022policyguided,
  title  = {A Policy-Guided Imitation Approach for Offline Reinforcement Learning},
  author = {Haoran Xu and Li Jiang and Jianxiong Li and Xianyuan Zhan},
  year   = {2022},
  booktitle = {Advances in Neural Information Processing Systems},
}