Model-Based Reinforcement Learning for Partially Observable Games with Sampling-Based State Estimation

DOI10.1162/neco.2007.19.11.3051zbMath1143.68536OpenAlexW1992679782WikidataQ51977186 ScholiaQ51977186MaRDI QIDQ5441307

Shin Ishii, Hajime Fujita

Publication date: 11 February 2008

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: http://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=100048469&oldid=89222

zbMATH Keywords

multiagent problem

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) (n)-person games, (n>2) (91A06) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (1)

An information-theoretic analysis of return maximization in reinforcement learning

Cites Work

• Planning and acting in partially observable stochastic domains
• Elevator group control using multiple reinforcement learning agents
• The lagging anchor algorithm: Reinforcement learning in two-player zero-sum games with imperfect information
• Linear least-squares algorithms for temporal difference learning
• The Optimal Control of Partially Observable Markov Processes over a Finite Horizon
• Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning
• Multiple Model-Based Reinforcement Learning
• Recent advances in hierarchical reinforcement learning
• A reinforcement learning scheme for a partially-observable multi-agent game

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