  Learning a precise dynamics model can be crucial for offline reinforcement
learning, which, unfortunately, has been found to be quite challenging.
Dynamics models that are learned by fitting historical transitions often
struggle to generalize to unseen transitions. In this study, we identify a
hidden but pivotal factor termed dynamics reward that remains consistent across
transitions, offering a pathway to better generalization. Therefore, we propose
the idea of reward-consistent dynamics models: any trajectory generated by the
dynamics model should maximize the dynamics reward derived from the data. We
implement this idea as the MOREC (Model-based Offline reinforcement learning
with Reward Consistency) method, which can be seamlessly integrated into
previous offline model-based reinforcement learning (MBRL) methods. MOREC
learns a generalizable dynamics reward function from offline data, which is
subsequently employed as a transition filter in any offline MBRL method: when
generating transitions, the dynamics model generates a batch of transitions and
selects the one with the highest dynamics reward value. On a synthetic task, we
visualize that MOREC has a strong generalization ability and can surprisingly
recover some distant unseen transitions. On 21 offline tasks in D4RL and NeoRL
benchmarks, MOREC improves the previous state-of-the-art performance by a
significant margin, i.e., 4.6% on D4RL tasks and 25.9% on NeoRL tasks. Notably,
MOREC is the first method that can achieve above 95% online RL performance in 6
out of 12 D4RL tasks and 3 out of 9 NeoRL tasks.
