AI RESEARCH
Evolving Robustness--Exploration Trade-off in Online Reinforcement Learning via Quantile Bayesian Risk MDPs
arXiv CS.LG
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ArXi:2605.24345v1 Announce Type: new In online reinforcement learning, data scarcity creates epistemic uncertainty that makes robustness important early in learning, whereas sufficient exploration is needed to learn the true-environment optimal policy. We study this time-varying robustness--exploration trade-off through a quantile Bayesian risk-aware Marko decision process (BR-MDP), in which the quantile level controls how posterior uncertainty enters the Bellman backup.