Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path

DOI10.1007/11776420_42zbMath1143.68516OpenAlexW1557941966MaRDI QIDQ5307594

Csaba Szepesvári, András Antos, Rémi Munos

Publication date: 14 September 2007

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

Full work available at URL: https://doi.org/10.1007/11776420_42

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

An incremental off-policy search in a model-free Markov decision process using a single sample path ⋮ A unified DC programming framework and efficient DCA based approaches for large scale batch reinforcement learning ⋮ Approximation of Markov decision processes with general state space ⋮ Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path ⋮ Multi-agent reinforcement learning: a selective overview of theories and algorithms

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