Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness
Classical reinforcement learning assumes the agent interacts with a fixed environment whose behavior does not depend on the agent's policy. This assumption breaks down in non-realizable settings where other actors might anticipate the agent's behavior, including environments crucial to AI safety, where the agent interacts with predictors, humans, other AI agents, and institutions. In such settings, the agent's model class fails to capture the world in which it operates. Under such misspecification, classical Bayesian methods can produce confidently wrong posteriors, unreliable decisions, and unbounded regret, as realizability fails to obtain. Infra-Bayesianism is a decision-theoretic fram...
| Authors | Manish Aryal, Faiyaz Azam, Agnivo Banerjee et al. |
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| Primary category | cs.LG |
| Published | 22 May 2026 |
| Last updated at source | 11 August 2026 |
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