Near-optimal PAC bounds for discounted MDPs

DOI10.1016/j.tcs.2014.09.029zbMath1360.68528OpenAlexW1969276875WikidataQ58012230 ScholiaQ58012230MaRDI QIDQ465258

Tor Lattimore, Marcus Hutter

Publication date: 31 October 2014

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2014.09.029

zbMATH Keywords

Markov decision processesreinforcement learningPAC boundssample-complexity

Mathematics Subject Classification ID

Computational learning theory (68Q32) Markov and semi-Markov decision processes (90C40)

Related Items (2)

Extreme state aggregation beyond Markov decision processes ⋮ Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis

Cites Work

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• Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model
• An analysis of model-based interval estimation for Markov decision processes
• Asymptotically efficient adaptive allocation rules
• Bayesian Reinforcement Learning with Exploration
• PAC Bounds for Discounted MDPs
• Concentration Inequalities and Martingale Inequalities: A Survey
• The variance of discounted Markov decision processes

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