  In offline reinforcement learning, the challenge of out-of-distribution (OOD)
is pronounced. To address this, existing methods often constrain the learned
policy through policy regularization. However, these methods often suffer from
the issue of unnecessary conservativeness, hampering policy improvement. This
occurs due to the indiscriminate use of all actions from the behavior policy
that generates the offline dataset as constraints. The problem becomes
particularly noticeable when the quality of the dataset is suboptimal. Thus, we
propose Adaptive Advantage-guided Policy Regularization (A2PR), obtaining
high-advantage actions from an augmented behavior policy combined with VAE to
guide the learned policy. A2PR can select high-advantage actions that differ
from those present in the dataset, while still effectively maintaining
conservatism from OOD actions. This is achieved by harnessing the VAE capacity
to generate samples matching the distribution of the data points. We
theoretically prove that the improvement of the behavior policy is guaranteed.
Besides, it effectively mitigates value overestimation with a bounded
performance gap. Empirically, we conduct a series of experiments on the D4RL
benchmark, where A2PR demonstrates state-of-the-art performance. Furthermore,
experimental results on additional suboptimal mixed datasets reveal that A2PR
exhibits superior performance. Code is available at
https://github.com/ltlhuuu/A2PR.
