@inproceedings{
kim2024adaptive,
title={Adaptive \$Q\$-Aid for Conditional Supervised Learning in Offline Reinforcement Learning},
author={Jeonghye Kim and Suyoung Lee and Woojun Kim and Youngchul Sung},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3HpgVs22UJ}
}
Offline reinforcement learning (RL) has progressed with return-conditioned supervised learning (RCSL), but its lack of stitching ability remains a limitation. We introduce $Q$-Aided Conditional Supervised Learning (QCS), which effectively combines the stability of RCSL with the stitching capability of $Q$-functions. By analyzing $Q$-function over-generalization, which impairs stable stitching, QCS adaptively integrates $Q$-aid into RCSL's loss function based on trajectory return. Empirical results show that QCS significantly outperforms RCSL and value-based methods, consistently achieving or exceeding the highest trajectory returns across diverse offline RL benchmarks. QCS represents a breakthrough in offline RL, pushing the limits of what can be achieved and fostering further innovations.
Adaptive Q-Aid for Conditional Supervised Learning in Offline Reinforcement Learning
Jeonghye Kim Suyoung Lee Woojun Kim Youngchul Sung