  Offline reinforcement learning enables learning from a fixed dataset, without
further interactions with the environment. The lack of environmental
interactions makes the policy training vulnerable to state-action pairs far
from the training dataset and prone to missing rewarding actions. For training
more effective agents, we propose a framework that supports learning a flexible
yet well-regularized fully-implicit policy. We further propose a simple
modification to the classical policy-matching methods for regularizing with
respect to the dual form of the Jensen--Shannon divergence and the integral
probability metrics. We theoretically show the correctness of the
policy-matching approach, and the correctness and a good finite-sample property
of our modification. An effective instantiation of our framework through the
GAN structure is provided, together with techniques to explicitly smooth the
state-action mapping for robust generalization beyond the static dataset.
Extensive experiments and ablation study on the D4RL benchmark validate our
framework and the effectiveness of our algorithmic designs.
