Offline Reinforcement Learning with On-Policy Q-Function Regularization

• URL: http://arxiv.org/abs/2307.13824v1
• Date: Tue, 25 Jul 2023 21:38:08 GMT
• Title: Offline Reinforcement Learning with On-Policy Q-Function Regularization
• Authors: Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist
• Abstract summary: We deal with the (potentially catastrophic) extrapolation error induced by the distribution shift between the history dataset and the desired policy. We propose two algorithms taking advantage of the estimated Q-function through regularizations, and demonstrate they exhibit strong performance on the D4RL benchmarks.
• Score: 57.09073809901382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The core challenge of offline reinforcement learning (RL) is dealing with the (potentially catastrophic) extrapolation error induced by the distribution shift between the history dataset and the desired policy. A large portion of prior work tackles this challenge by implicitly/explicitly regularizing the learning policy towards the behavior policy, which is hard to estimate reliably in practice. In this work, we propose to regularize towards the Q-function of the behavior policy instead of the behavior policy itself, under the premise that the Q-function can be estimated more reliably and easily by a SARSA-style estimate and handles the extrapolation error more straightforwardly. We propose two algorithms taking advantage of the estimated Q-function through regularizations, and demonstrate they exhibit strong performance on the D4RL benchmarks.

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