Invited Speaker: Emilie Kaufmann: On pure-exploration in Markov Decision Processes
Emilie Kaufmann
2021 Presentation
in
Workshop: Workshop on Reinforcement Learning Theory
in
Workshop: Workshop on Reinforcement Learning Theory
Abstract
This talk will be about some recent works on the sample complexity of pure-exploration in an episodic MDP, when we sequentially collect trajectories, with or without rewards, and aim at identifying good policies. In particular, I will describe an optimal algorithm for reward-free exploration called RF-Express, which interestingly features some exploration bonuses that scale in 1/n instead of the usual 1/\sqrt{n}.
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