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Worst-Case Regret Bounds for Exploration via Randomized Value Functions

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arxiv 1906.02870 v3 pith:HHJDJ43D submitted 2019-06-07 cs.LG cs.AIcs.SYeess.SYstat.ML

Daniel Russo This is my paper · ORCID
classification cs.LGcs.AIcs.SYeess.SYstat.ML
keywords functionsvaluerandomizedexplorationregretworst-caseapproachbound
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This paper studies a recent proposal to use randomized value functions to drive exploration in reinforcement learning. These randomized value functions are generated by injecting random noise into the training data, making the approach compatible with many popular methods for estimating parameterized value functions. By providing a worst-case regret bound for tabular finite-horizon Markov decision processes, we show that planning with respect to these randomized value functions can induce provably efficient exploration.

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Cited by 1 Pith paper

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  1. Thompson Sampling in Online RLHF with General Function Approximation

    cs.LG 2025-05 reject novelty 6.0 of 10

    A model-free posterior sampling algorithm for online RLHF is shown to achieve O(sqrt(T)) regret when the completed function class has low Bellman eluder dimension.

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