Exploration of multi-state environments: Local measures and back-propagation of uncertainty

DOI10.1023/A:1007541107674zbMath0948.68094OpenAlexW1515933446MaRDI QIDQ1961327

Paul Bourgine, Nicolas Meuleau

Publication date: 22 November 2000

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

Full work available at URL: https://doi.org/10.1023/a:1007541107674

zbMATH Keywords

reinforcement learningglobal-scale reasoning

Mathematics Subject Classification ID

Computational learning theory (68Q32) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (3)

A novel policy based on action confidence limit to improve exploration efficiency in reinforcement learning ⋮ An asymptotically optimal policy for finite support models in the multiarmed bandit problem ⋮ A dynamic programming strategy to balance exploration and exploitation in the bandit problem

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