  Offline reinforcement learning (RL) optimizes the policy on a previously
collected dataset without any interactions with the environment, yet usually
suffers from the distributional shift problem. To mitigate this issue, a
typical solution is to impose a policy constraint on a policy improvement
objective. However, existing methods generally adopt a ``one-size-fits-all''
practice, i.e., keeping only a single improvement-constraint balance for all
the samples in a mini-batch or even the entire offline dataset. In this work,
we argue that different samples should be treated with different policy
constraint intensities. Based on this idea, a novel plug-in approach named
Guided Offline RL (GORL) is proposed. GORL employs a guiding network, along
with only a few expert demonstrations, to adaptively determine the relative
importance of the policy improvement and policy constraint for every sample. We
theoretically prove that the guidance provided by our method is rational and
near-optimal. Extensive experiments on various environments suggest that GORL
can be easily installed on most offline RL algorithms with statistically
significant performance improvements.
