Batch mode reinforcement learning based on the synthesis of artificial trajectories

DOI10.1007/s10479-012-1248-5zbMath1276.68134OpenAlexW2134689794WikidataQ42258641 ScholiaQ42258641MaRDI QIDQ378762

Damien Ernst, Louis Wehenkel, Raphael Fonteneau, Susan A. Murphy

Publication date: 12 November 2013

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3773886

zbMATH Keywords

optimal controlreinforcement learningfunction approximatorsartificial trajectories

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Stochastic learning and adaptive control (93E35)

Related Items

Lipschitzness is all you need to tame off-policy generative adversarial imitation learning

Uses Software

• Approxrl

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Kernel-based reinforcement learning
• Technical update: Least-squares temporal difference learning
• Least squares policy evaluation algorithms with linear function approximation
• Towards Min Max Generalization in Reinforcement Learning
• Marginal Mean Models for Dynamic Regimes
• Optimal Dynamic Treatment Regimes
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
• 10.1162/1532443041827907
• Machine Learning: ECML 2003