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Improved Analysis of UCRL2 with Empirical Bernstein Inequality

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arxiv 2007.05456 v1 pith:FUI2FKBT submitted 2020-07-10 cs.LG stat.ML

classification cs.LGstat.ML
keywords analysisbernsteinempiricalgammastatesucrl2ucrl2bactions
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abstract

We consider the problem of exploration-exploitation in communicating Markov Decision Processes. We provide an analysis of UCRL2 with Empirical Bernstein inequalities (UCRL2B). For any MDP with $S$ states, $A$ actions, $\Gamma \leq S$ next states and diameter $D$, the regret of UCRL2B is bounded as $\widetilde{O}(\sqrt{D\Gamma S A T})$.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Asymptotically optimal regret in communicating Markov decision processes

    cs.LG 2025-05 conditional novelty 7.0 of 10

    The paper claims the first asymptotically optimal regret algorithm, achieving the exact logarithmic constant K(M), for average-reward communicating Markov decision processes.

  2. A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Improved classical and quantum regret bounds for reinforcement learning with a generative model, including a new expected-regret measure under which quantum algorithms achieve polylogarithmic regret for infinite-horiz...

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