  Offline Reinforcement Learning (RL) aims to extract near-optimal policies
from imperfect offline data without additional environment interactions.
Extracting policies from diverse offline datasets has the potential to expand
the range of applicability of RL by making the training process safer, faster,
and more streamlined. We investigate how to improve the performance of offline
RL algorithms, its robustness to the quality of offline data, as well as its
generalization capabilities. To this end, we introduce Offline Model-based RL
with Adaptive Behavioral Priors (MABE). Our algorithm is based on the finding
that dynamics models, which support within-domain generalization, and
behavioral priors, which support cross-domain generalization, are
complementary. When combined together, they substantially improve the
performance and generalization of offline RL policies. In the widely studied
D4RL offline RL benchmark, we find that MABE achieves higher average
performance compared to prior model-free and model-based algorithms. In
experiments that require cross-domain generalization, we find that MABE
outperforms prior methods. Our website is available at
https://sites.google.com/berkeley.edu/mabe .
