  In offline reinforcement learning (RL), one detrimental issue to policy
learning is the error accumulation of deep Q function in out-of-distribution
(OOD) areas. Unfortunately, existing offline RL methods are often
over-conservative, inevitably hurting generalization performance outside data
distribution. In our study, one interesting observation is that deep Q
functions approximate well inside the convex hull of training data. Inspired by
this, we propose a new method, DOGE (Distance-sensitive Offline RL with better
GEneralization). DOGE marries dataset geometry with deep function approximators
in offline RL, and enables exploitation in generalizable OOD areas rather than
strictly constraining policy within data distribution. Specifically, DOGE
trains a state-conditioned distance function that can be readily plugged into
standard actor-critic methods as a policy constraint. Simple yet elegant, our
algorithm enjoys better generalization compared to state-of-the-art methods on
D4RL benchmarks. Theoretical analysis demonstrates the superiority of our
approach to existing methods that are solely based on data distribution or
support constraints.
