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As with any machine learning problem with limited data, effective offline RLalgorithms require careful regularization to avoid overfitting. One-stepmethods perform regularization by doing just a single step of policyimprovement, while critic regularization methods do many steps of policyimprovement with a regularized objective. These methods appear distinct.One-step methods, such as advantage-weighted regression and conditionalbehavioral cloning, truncate policy iteration after just one step. This ``earlystopping'' makes one-step RL simple and stable, but can limit its asymptoticperformance. Critic regularization typically requires more compute but hasappealing lower-bound guarantees. In this paper, we draw a close connectionbetween these methods: applying a multi-step critic regularization method witha regularization coefficient of 1 yields the same policy as one-step RL. Whilepractical implementations violate our assumptions and critic regularization istypically applied with smaller regularization coefficients, our experimentsnevertheless show that our analysis makes accurate, testable predictions aboutpractical offline RL methods (CQL and one-step RL) with commonly-usedhyperparameters. Our results that every problem can be solved with a singlestep of policy improvement, but rather that one-step RL might be competitivewith critic regularization on RL problems that demand strong regularization.

A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning