PAC Bounds for Discounted MDPs

DOI10.1007/978-3-642-34106-9_26zbMath1367.68233arXiv1202.3890OpenAlexW1867103660WikidataQ58012270 ScholiaQ58012270MaRDI QIDQ3164829

Marcus Hutter, Tor Lattimore

Publication date: 16 October 2012

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1202.3890

zbMATH Keywords

Markov decision processesreinforcement learningsample-complexityPAC-MDPexploration exploitation

Mathematics Subject Classification ID

General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model ⋮ Recent advances in reinforcement learning in finance ⋮ Near-optimal PAC bounds for discounted MDPs ⋮ Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes

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