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Offline Reinforcement Learning (RL) aims to extract near-optimal policiesfrom imperfect offline data without additional environment interactions.Extracting policies from diverse offline datasets has the potential to expandthe range of applicability of RL by making the training process safer, faster,and more streamlined. We investigate how to improve the performance of offlineRL algorithms, its robustness to the quality of offline data, as well as itsgeneralization capabilities. To this end, we introduce Offline Model-based RLwith Adaptive Behavioral Priors (MABE). Our algorithm is based on the findingthat dynamics models, which support within-domain generalization, andbehavioral priors, which support cross-domain generalization, arecomplementary. When combined together, they substantially improve theperformance and generalization of offline RL policies. In the widely studiedD4RL offline RL benchmark, we find that MABE achieves higher averageperformance compared to prior model-free and model-based algorithms. Inexperiments that require cross-domain generalization, we find that MABEoutperforms prior methods. Our website is available athttps://sites.google.com/berkeley.edu/mabe .

Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL