An Implicit Trust Region Approach to Behavior Regularized Offline Reinforcement Learning
DOI:
https://doi.org/10.1609/aaai.v38i15.29637Keywords:
ML: Reinforcement LearningAbstract
We revisit behavior regularization, a popular approach to mitigate the extrapolation error in offline reinforcement learning (RL), showing that current behavior regularization may suffer from unstable learning and hinder policy improvement. Motivated by this, a novel reward shaping-based behavior regularization method is proposed, where the log-probability ratio between the learned policy and the behavior policy is monitored during learning. We show that this is equivalent to an implicit but computationally lightweight trust region mechanism, which is beneficial to mitigate the influence of estimation errors of the value function, leading to more stable performance improvement. Empirical results on the popular D4RL benchmark verify the effectiveness of the presented method with promising performance compared with some state-of-the-art offline RL algorithms.Downloads
Published
2024-03-24
How to Cite
Zhang, Z., & Tan, X. (2024). An Implicit Trust Region Approach to Behavior Regularized Offline Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(15), 16944-16952. https://doi.org/10.1609/aaai.v38i15.29637
Issue
Section
AAAI Technical Track on Machine Learning VI