Offline Policy Optimization with Posterior Sampling.

arXiv:2605.07393v2 Announce Type: replace Abstract: A fundamental challenge in model-based offline reinforcement learning (RL) lies in the trade-off between generalization and robustness against exploitation errors in out-of-distribution (OOD) regions. The key