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We study the tradeoff between consistency and robustness in the context of asingle-trajectory time-varying Markov Decision Process (MDP) with untrustedmachine-learned advice. Our work departs from the typical approach of treatingadvice as coming from black-box sources by instead considering a setting whereadditional information about how the advice is generated is available. We provea first-of-its-kind consistency and robustness tradeoff given Q-value adviceunder a general MDP model that includes both continuous and discretestate/action spaces. Our results highlight that utilizing Q-value adviceenables dynamic pursuit of the better of machine-learned advice and a robustbaseline, thus result in near-optimal performance guarantees, which provablyimproves what can be obtained solely with black-box advice.

Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions