Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief

arXiv:2606.00680v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) aims to optimize policies from pre-collected datasets. A bottleneck of this paradigm is managing epistemic uncertainty, which arises from limited data coverage (sample-level) and the ambiguity in identifying transition dynamics from finite data (model-level)....

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