MoMA: Model-based Mirror Ascent for Offline Reinforcement Learning.

ABSTRACT: Model-based offline reinforcement learning methods (RL) have achieved state-of-the-art performance in many decision-making problems thanks to their sample efficiency and generalizability. Despite these advancements, existing model-based offline RL approaches either focus on theoretical studies without developing practical algorithms or rely on a restricted parametric policy space, thus not fully leveraging the advantages of an unrestricted policy space inherent to model-based methods. To address this limitation, we develop MoMA, a model-based mirror ascent algorithm with general function approximations under partial coverage of offline data. MoMA distinguishes itself from existing literature by employing an unrestricted policy class. In each iteration, MoMA conservatively estimates the val

SUBMITTER: Hong M 

PROVIDER: S-EPMC12742664 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature