Exclusively Penalized Q-learning for Offline Reinforcement Learning

Junghyuk Yeom, Yonghyeon Jo, Jeongmo Kim, Sanghyeon Lee, Seungyul Han

Keywords: Deep reinforcement learning, offline RL, Q-learning, overestimation reduction

TL;DR: We propose a novel penalizing method, which efficiently reduces overestimation for offline reinforcement learning

Abstract: Constraint-based offline reinforcement learning (RL) involves policy constraints or imposing penalties on the value function to mitigate overestimation errors caused by distributional shift. This paper focuses on a limitation in existing offline RL methods with penalized value function, indicating the potential for underestimation bias due to unnecessary bias introduced in the value function. To address this concern, we propose Exclusively Penalized Q-learning (EPQ), which reduces estimation bias in the value function by selectively penalizing states that are prone to inducing estimation errors. Numerical results show that our method significantly reduces underestimation bias and improves performance in various offline control tasks compared to other offline RL methods.

Primary Area: Reinforcement learning

Submission Number: 9935