  As with any machine learning problem with limited data, effective offline RL
algorithms require careful regularization to avoid overfitting. One-step
methods perform regularization by doing just a single step of policy
improvement, while critic regularization methods do many steps of policy
improvement with a regularized objective. These methods appear distinct.
One-step methods, such as advantage-weighted regression and conditional
behavioral cloning, truncate policy iteration after just one step. This ``early
stopping'' makes one-step RL simple and stable, but can limit its asymptotic
performance. Critic regularization typically requires more compute but has
appealing lower-bound guarantees. In this paper, we draw a close connection
between these methods: applying a multi-step critic regularization method with
a regularization coefficient of 1 yields the same policy as one-step RL. While
practical implementations violate our assumptions and critic regularization is
typically applied with smaller regularization coefficients, our experiments
nevertheless show that our analysis makes accurate, testable predictions about
practical offline RL methods (CQL and one-step RL) with commonly-used
hyperparameters. Our results that every problem can be solved with a single
step of policy improvement, but rather that one-step RL might be competitive
with critic regularization on RL problems that demand strong regularization.
