SCORE: Spurious COrrelation REduction for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to learn the optimal policy from a pre-collected dataset without online interactions. Most of the existing studies focus on distributional shift caused by out-of-distribution actions. However, even in-distribution actions can raise serious problems. Since the dataset only contains limited information about the underlying model, offline RL is vulnerable to spurious correlations, i.e., the agent tends to prefer actions that by chance lead to high returns, resulting in a highly suboptimal policy. To address such a challenge, we propose a practical and theoretically guaranteed algorithm SCORE that reduces spurious correlations by combing an uncertainty penalty into policy evaluation. We show that this is consistent with the pessimism principle studied in theory, and the proposed algorithm converges to the optimal policy with a sublinear rate under mild assumptions. By conducting extensive experiments on existing benchmarks, we show that SCORE not only benefits from a solid theory but also obtains strong empirical results on a variety of tasks.