Hybrid least-squares algorithms for approximate policy evaluation

DOI10.1007/s10994-009-5128-4zbMath1470.68124OpenAlexW4236439427WikidataQ115146324 ScholiaQ115146324MaRDI QIDQ1959511

Marek Petrik, Jeff Johns, Sridhar Mahadevan

Publication date: 7 October 2010

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-009-5128-4

zbMATH Keywords

Markov decision processesreinforcement learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Markov and semi-Markov decision processes (90C40)

Related Items (1)

Reinforcement learning algorithms with function approximation: recent advances and applications

Cites Work

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• Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
• Generalized polynomial approximations in Markovian decision processes
• 10.1162/1532443041827907

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