Rollout sampling approximate policy iteration

DOI10.1007/S10994-008-5069-3zbMath1464.68308OpenAlexW3099235411MaRDI QIDQ2036256

Christos Dimitrakakis, Michail G. Lagoudakis

Publication date: 28 June 2021

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

Full work available at URL: https://doi.org/10.1007/s10994-008-5069-3

zbMATH Keywords

classificationreinforcement learningrolloutsbandit problemssample complexityapproximate policy iteration

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Sampling theory, sample surveys (62D05) Learning and adaptive systems in artificial intelligence (68T05) Optimal stopping in statistics (62L15)

Related Items (3)

Dynamic parcel pick-up routing problem with prioritized customers and constrained capacity via lower-bound-based rollout approach ⋮ Preference-based reinforcement learning: a formal framework and a policy iteration algorithm ⋮ Unnamed Item

Cites Work

• Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
• 10.1162/1532443041827907
• Finite-time analysis of the multiarmed bandit problem
• Unnamed Item
• Unnamed Item
• Unnamed Item

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