New Research Proposes Bipredictability Metric for Monitoring Deployed Reinforcement Learning Agents

A research paper introduces Bipredictability (P), a novel metric for monitoring deployed reinforcement learning agents. The work argues that current monitoring approaches relying on reward and task metrics are reactive and fail to detect structural degradation before performance collapse. Published on arXiv with identifier 2603.01283v2, the paper frames deployment monitoring as a question of uncertainty resolution. Information theory provides the foundation, with entropy quantifying uncertainty and mutual information measuring its resolution across the observation-action-outcome loop. The proposed Bipredictability metric calculates the fraction of total uncertainty converted into shared predictability across this closed-loop system. This theoretical property offers a provable classical measure of interaction efficiency. The research addresses the challenge of maintaining reliable performance in deployed RL agents operating in closed-loop environments where coherent coupling between observations, actions, and outcomes is essential.