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arXiv · 1210.4843

Deterministic MDPs with Adversarial Rewards and Bandit Feedback

Raman Arora·Ofer Dekel·Ambuj Tewari
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Abstract

We consider a Markov decision process with deterministic state transition dynamics, adversarially generated rewards that change arbitrarily from round to round, and a bandit feedback model in which the decision maker only observes the rewards it receives. In this setting, we present a novel and efficient online decision making algorithm named MarcoPolo. Under mild assumptions on the structure of the transition dynamics, we prove that MarcoPolo enjoys a regret of O(T^(3/4)sqrt(log(T))) against the best deterministic policy in hindsight. Specifically, our analysis does not rely on the stringent unichain assumption, which dominates much of the previous work on this topic.

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Raman Arora, Ofer Dekel, Ambuj Tewari. 2012-10-16. Deterministic MDPs with Adversarial Rewards and Bandit Feedback. https://arxiv.org/abs/1210.4843

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