Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief 事件

Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief arXiv:2606.00680v1 Announce Type: new 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). To provide a unified quantification of these uncertainties, Bayesian RL has b