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Offline reinforcement learning aims to utilize datasets of previouslygathered environment-action interaction records to learn a policy withoutaccess to the real environment. Recent work has shown that offlinereinforcement learning can be formulated as a sequence modeling problem andsolved via supervised learning with approaches such as decision transformer.While these sequence-based methods achieve competitive results overreturn-to-go methods, especially on tasks that require longer episodes or withscarce rewards, importance sampling is not considered to correct the policybias when dealing with off-policy data, mainly due to the absence of behaviorpolicy and the use of deterministic evaluation policies. To this end, wepropose DPE: an RL algorithm that blends offline sequence modeling and offlinereinforcement learning with Double Policy Estimation (DPE) in a unifiedframework with statistically proven properties on variance reduction. Wevalidate our method in multiple tasks of OpenAI Gym with D4RL benchmarks. Ourmethod brings a performance improvements on selected methods which outperformsSOTA baselines in several tasks, demonstrating the advantages of enablingdouble policy estimation for sequence-modeled reinforcement learning.

Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning