Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief
Summary
This paper introduces Posterior Hybrid Bayesian Belief (PhyB), a framework that reformulates the expectation in Bayesian RL as a convex combination over dynamics models, enabling efficient regularized offline policy optimization with bounded objective discrepancy and state-of-the-art performance.
View Cached Full Text
Cached at: 06/02/26, 03:48 PM
# Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief Source: [https://arxiv.org/abs/2606.00680](https://arxiv.org/abs/2606.00680) [View PDF](https://arxiv.org/pdf/2606.00680) > 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 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 with poor computational scalability or impose restrictive posterior assumptions that sacrifice the adaptability of Bayesian RL\. To address these limitations, we propose Posterior Hybrid Bayesian Belief \(PhyB\), which reformulates the expectation as a convex combination over a subset of dynamics models\. Theoretical analysis demonstrates that the objective discrepancy induced by this approximation remains bounded\. Based on PhyB, we develop an iterative regularized policy optimization algorithm that provides metric\-agnostic guarantees for monotonic improvement until convergence\. Empirical results demonstrate that PhyB achieves state\-of\-the\-art performance on various benchmarks\. ## Submission history From: Hongqiang Lin \[[view email](https://arxiv.org/show-email/d0fd1c71/2606.00680)\] **\[v1\]**Sat, 30 May 2026 11:35:26 UTC \(3,720 KB\)
Similar Articles
From Static Policies to Adaptive Priors in Offline Reinforcement Learning
This position paper argues that offline RL should shift from learning static deployment policies to learning adaptive policy priors that can improve through subsequent online interaction, proposing a framework called Adaptive Offline RL (AORL) and highlighting Bayesian offline RL as a principled approach to preserving epistemic uncertainty.
Generative OOD-regularized Model-based Policy Optimization
Introduces GORMPO, a density-regularized offline RL algorithm that uses generative density modeling to restrict policy updates to high-density areas, achieving 17% improvement on a real-world medical dataset and outperforming state-of-the-art baselines.
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
Proposes ERAHBO, an efficient heteroscedastic Bayesian optimization method for risk-aware hyperparameter optimization in reinforcement learning, using adaptive re-sampling to improve sample efficiency over fixed-budget approaches.
Robust Data-Collection Policy Learning for Low-Variance Online Policy Evaluation
This paper proposes a robust gradient-based algorithm for learning behavior policies that reduce variance in online reinforcement learning policy evaluation, addressing uncertainties in transition functions with theoretical guarantees and numerical validation.
Bellman Policy Optimization
Bellman Policy Optimization (BPO) is a critic-free reinforcement learning method that reformulates Policy Mirror Descent using the Bellman equation for autoregressive generation with terminal rewards, improving mathematical reasoning in large language models.