  Most prior approaches to offline reinforcement learning (RL) have taken an
iterative actor-critic approach involving off-policy evaluation. In this paper
we show that simply doing one step of constrained/regularized policy
improvement using an on-policy Q estimate of the behavior policy performs
surprisingly well. This one-step algorithm beats the previously reported
results of iterative algorithms on a large portion of the D4RL benchmark. The
one-step baseline achieves this strong performance while being notably simpler
and more robust to hyperparameters than previously proposed iterative
algorithms. We argue that the relatively poor performance of iterative
approaches is a result of the high variance inherent in doing off-policy
evaluation and magnified by the repeated optimization of policies against those
estimates. In addition, we hypothesize that the strong performance of the
one-step algorithm is due to a combination of favorable structure in the
environment and behavior policy.
