Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness
A new study presents infra-Bayesian reinforcement learning agents that outperform traditional methods in scenarios with model misspecification. These agents utilize a decision-theoretic framework that focuses on worst-case outcomes rather than expected values. The findings suggest improved robustness in environments with Knightian uncertainty, demonstrating lower worst-case regret compared to classical reinforcement learning agents.
- ▪Classical reinforcement learning assumes a fixed environment, which can lead to failures in non-realizable settings.
- ▪Infra-Bayesianism distinguishes between probabilistic uncertainty and Knightian uncertainty, evaluating actions based on worst-case outcomes.
- ▪The study shows that infra-Bayesian agents achieve lower worst-case regret and optimal strategies in complex decision-making scenarios.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23146 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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Computer Science > Machine Learning arXiv:2605.23146 (cs) [Submitted on 22 May 2026] Title:Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness Authors:Manish Aryal, Faiyaz Azam, Agnivo Banerjee, Sai Sidhanth Manoharan Jayanthi, Allegra Laro, Clément Legentilhomme, Andrew Lin, Florian Lorkowski, Radman Rakhshandehroo, Patric Rommel, Emanuel Ruzak, Nathan Theng, Paul Yushin Rapoport View a PDF of the paper titled Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness, by Manish Aryal and 12 other authors View PDF HTML (experimental) Abstract:Classical reinforcement learning assumes the agent interacts with a fixed environment whose behavior does not depend on the agent's policy.
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