Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief
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 been proposed by treating the dynamics model as a random variable and maintaining a corresponding belief. Despite its theoretical appeal, policy optimization in Bayesian RL remains computationally challenging as it requires solving composite objectives with expectations. Prior methods either employ search-based techniques w...
| Authors | Hongqiang Lin, Pengfei Wang, Nenggan Zheng |
|---|---|
| Primary category | cs.AI |
| Published | 30 May 2026 |
| Last updated at source | 18 July 2026 |
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