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 misspecificati

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ethics.ai (22 May 2026), “Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness,” evidence record 3895, https://ethics.ai/record/3895 (originally published by arXiv).

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