OpenNovelty.org

EUBRL: Epistemic Uncertainty Directed Bayesian Reinforcement Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Bayesian RLepistemic uncertaintyexploration
Abstract:

At the boundary between the known and the unknown, an agent inevitably confronts the dilemma of whether to explore or to exploit. Epistemic uncertainty reflects such boundaries, representing systematic uncertainty due to limited knowledge. In this paper, we propose a Bayesian reinforcement learning (RL) algorithm, EUBRL\texttt{EUBRL}, which leverages epistemic guidance to achieve principled exploration. This guidance adaptively reduces per-step regret arising from estimation errors. We establish nearly minimax-optimal regret and sample complexity guarantees for a specific class of priors in infinite-horizon discounted MDPs. Empirically, we evaluate EUBRL\texttt{EUBRL} on tasks characterized by sparse rewards, long horizons, and stochasticity. Results demonstrate that EUBRL\texttt{EUBRL} achieves superior sample efficiency, scalability, and consistency.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes EUBRL, a Bayesian reinforcement learning algorithm that uses epistemic uncertainty to guide exploration and reduce per-step regret from estimation errors. It resides in the 'Bayesian Neural Network Approaches' leaf under 'Epistemic Uncertainty Estimation Methods', alongside three sibling papers. This leaf represents one of three parallel approaches to uncertainty estimation in the taxonomy, suggesting a moderately populated but not overcrowded research direction. The taxonomy contains fifty papers total across approximately thirty-six topics, indicating that Bayesian methods constitute a focused but established subfield within epistemic uncertainty-guided exploration.

The taxonomy reveals that EUBRL's leaf sits within a broader branch of uncertainty estimation methods, with sibling leaves covering ensemble-based techniques and uncertainty decomposition frameworks. Neighboring branches address how uncertainty estimates drive exploration strategies (optimistic bonuses, information-theoretic approaches, Thompson sampling) and model-based planning with epistemic guidance. The taxonomy's scope notes clarify that Bayesian methods emphasize posterior inference and principled uncertainty propagation, distinguishing them from ensemble approaches that approximate epistemic uncertainty through prediction disagreement. EUBRL's theoretical focus on regret bounds also connects it to the 'Theoretical and Empirical Foundations' branch, which formalizes exploration-exploitation trade-offs.

Among sixteen candidates examined across three contributions, no refutable prior work was identified. The core EUBRL algorithm examined ten candidates with zero refutations, the regret guarantees examined five candidates with zero refutations, and the prior-dependent bounds examined one candidate with zero refutations. This limited search scope—covering top-K semantic matches and citation expansion rather than exhaustive review—suggests that within the examined literature, the specific combination of Bayesian epistemic guidance, adaptive regret reduction, and minimax-optimal guarantees appears distinct. The theoretical contribution on prior-dependent bounds received minimal examination (one candidate), indicating either sparse prior work in this specific formulation or limited retrieval coverage.

Based on the examined thirty-paper subset from a fifty-paper taxonomy, EUBRL appears to occupy a recognizable niche combining Bayesian uncertainty estimation with principled exploration guarantees. The absence of refutations among sixteen candidates suggests novelty in the specific algorithmic and theoretical package, though the limited search scope means potentially relevant work in adjacent leaves (ensemble methods, information-theoretic exploration) may not have been fully captured. The taxonomy structure indicates active research in related directions, positioning EUBRL as an incremental but substantive contribution within an established research area.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
16
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: epistemic uncertainty guided exploration in reinforcement learning. The field organizes around several complementary perspectives on how agents can leverage knowledge about what they do not know to improve learning efficiency and safety. At the highest level, the taxonomy distinguishes methods for estimating epistemic uncertainty (including Bayesian neural network approaches, ensemble techniques, and distributional methods) from strategies that use these estimates to guide exploration (such as information-gain bonuses, optimism under uncertainty, and risk-sensitive policies). Parallel branches address model-based RL with uncertainty-aware dynamics models, safe RL that uses epistemic bounds to avoid dangerous states, and offline or robust settings where uncertainty quantification becomes critical for generalization. Works like Reward Uncertainty for Exploration[2] and Exploration via epistemic value[7] illustrate how uncertainty estimates can directly shape exploration bonuses, while Safe reinforcement learning with[1] and ActSafe[19] demonstrate applications in safety-critical domains. Additional branches cover multi-agent coordination, preference-based learning, and theoretical foundations that formalize the exploration-exploitation trade-off under epistemic constraints. Several active research directions reveal contrasting emphases and open questions. One line focuses on scalable uncertainty estimation in deep networks, balancing computational cost against the fidelity of epistemic quantification; for instance, ensemble methods offer practical approximations while Bayesian approaches like those in EUBRL[0] aim for principled posterior inference. Another theme concerns the interplay between model-based planning and uncertainty: Planning with Uncertainty[5] and Ocean-mbrl[33] explore how epistemic estimates in learned dynamics can guide both exploration and safe decision-making. EUBRL[0] sits within the Bayesian neural network branch, emphasizing rigorous uncertainty propagation for exploration, closely related to Guiding Reinforcement Learning Using[24] and Uncertainty-Guided Active Reinforcement Learning[41], which similarly leverage epistemic signals to prioritize informative state-action regions. Compared to ensemble-based or information-theoretic alternatives like MaxInfoRL[9], EUBRL[0] trades off computational overhead for more coherent uncertainty estimates, reflecting an ongoing debate about the right granularity and computational budget for epistemic guidance in modern RL systems.

Claimed Contributions

EUBRL algorithm with epistemic guidance for principled exploration

The authors introduce EUBRL, a Bayesian RL algorithm that uses epistemically guided rewards derived from probabilistic inference. This guidance adaptively balances exploration and exploitation by disentangling them through a probability of uncertainty term, making the method more resilient to unreliable reward estimates.

10 retrieved papers
Nearly minimax-optimal regret and sample complexity guarantees

The authors prove theoretical guarantees showing that EUBRL achieves nearly minimax-optimal bounds on both regret and sample complexity for infinite-horizon discounted MDPs under a class of priors. This is claimed as the first such sample complexity result without assuming a generative model.

5 retrieved papers
Prior-dependent bounds with conjugate prior instantiations

The authors extend their theoretical framework from frequentist to Bayesian settings by deriving prior-dependent bounds for a specific class of priors. They demonstrate concrete applications using commonly used conjugate priors such as Dirichlet for transitions and Normal or Normal-Gamma for rewards.

1 retrieved paper

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

EUBRL algorithm with epistemic guidance for principled exploration

The authors introduce EUBRL, a Bayesian RL algorithm that uses epistemically guided rewards derived from probabilistic inference. This guidance adaptively balances exploration and exploitation by disentangling them through a probability of uncertainty term, making the method more resilient to unreliable reward estimates.

Contribution

Nearly minimax-optimal regret and sample complexity guarantees

The authors prove theoretical guarantees showing that EUBRL achieves nearly minimax-optimal bounds on both regret and sample complexity for infinite-horizon discounted MDPs under a class of priors. This is claimed as the first such sample complexity result without assuming a generative model.

Contribution

Prior-dependent bounds with conjugate prior instantiations

The authors extend their theoretical framework from frequentist to Bayesian settings by deriving prior-dependent bounds for a specific class of priors. They demonstrate concrete applications using commonly used conjugate priors such as Dirichlet for transitions and Normal or Normal-Gamma for rewards.

Table of Contents