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Poster

State-free Reinforcement Learning

Mingyu Chen · Aldo Pacchiano · Xuezhou Zhang

West Ballroom A-D #6702
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[ Paper [ Poster [ OpenReview
Thu 12 Dec 11 a.m. PST — 2 p.m. PST

Abstract: In this work, we study the \textit{state-free RL} problem, where the algorithm does not have the states information before interacting with the environment. Specifically, denote the reachable state set by SΠ:={s|maxπΠqP,π(s)>0}, we design an algorithm which requires no information on the state space S while having a regret that is completely independent of S and only depend on SΠ. We view this as a concrete first step towards \textit{parameter-free RL}, with the goal of designing RL algorithms that require no hyper-parameter tuning.

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