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Conference Papers Year : 2021

Adaptive reward-free exploration

Emilie Kaufmann
Pierre Ménard
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Omar Darwiche Domingues
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Edouard Leurent
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Michal Valko


Reward-free exploration is a reinforcement learning setting recently studied by Jin et al., who address it by running several algorithms with regret guarantees in parallel. In our work, we instead propose a more adaptive approach for reward-free exploration which directly reduces upper bounds on the maximum MDP estimation error. We show that, interestingly, our reward-free UCRL algorithm can be seen as a variant of an algorithm of Fiechter from 1994 [11], originally proposed for a different objective that we call best-policy identification. We prove that RF-UCRL needs O (SAH 4 /ε 2) log(1/δ) episodes to output, with probability 1 − δ, an ε-approximation of the optimal policy for any reward function. We empirically compare it to oracle strategies using a generative model.
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Dates and versions

hal-02864574 , version 1 (11-06-2020)


  • HAL Id : hal-02864574 , version 1


Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, et al.. Adaptive reward-free exploration. Algorithmic Learning Theory, 2021, Paris, France. ⟨hal-02864574⟩
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