Web: http://arxiv.org/abs/2203.12922

June 17, 2022, 1:11 a.m. | Zihan Zhang, Xiangyang Ji, Simon S. Du

cs.LG updates on arXiv.org arxiv.org

This paper gives the first polynomial-time algorithm for tabular Markov
Decision Processes (MDP) that enjoys a regret bound \emph{independent on the
planning horizon}. Specifically, we consider tabular MDP with $S$ states, $A$
actions, a planning horizon $H$, total reward bounded by $1$, and the agent
plays for $K$ episodes. We design an algorithm that achieves an
$O\left(\mathrm{poly}(S,A,\log K)\sqrt{K}\right)$ regret in contrast to
existing bounds which either has an additional $\mathrm{polylog}(H)$
dependency~\citep{zhang2020reinforcement} or has an exponential dependency on
$S$~\citep{li2021settling}. Our result …

arxiv free learning lg polynomial power reinforcement reinforcement learning time

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