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分類 国際会議
著者名 (author) Hiroshi Osada,Satoshi Fujita
英文著者名 (author)
編者名 (editor)
編者名 (英文)
キー (key)
表題 (title) {CHQ}: A Multi-Agent Reinforcement Learning Scheme for Partially Observable Markov Decision Processes
表題 (英文)
書籍・会議録表題 (booktitle) Proc. IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'04)
書籍・会議録表題(英文)
巻数 (volume)
号数 (number)
ページ範囲 (pages) 17-23
組織名 (organization)
出版元 (publisher)
出版元 (英文)
出版社住所 (address)
刊行月 (month) September
出版年 (year) 2004
付加情報 (note) Beijing
注釈 (annote) DOI: 10.1109/IAT.2004.1342918, Acceptance rate: 44/266 = 17%
内容梗概 (abstract) We propose a reinforcement learning scheme called CHQ that could efficiently acquire appropriate policies under partially observable Markov decision processes (POMDP) involving probabilistic state transitions, that frequently occurs in multiagent systems in which each agent independently takes a probabilistic action based on a partial observation of the underlying environment. A key idea of CHQ is to extend the HQ-learning proposed by Wiering et al. in such a way that it could learn the activation order of the MDP subtasks as well as an appropriate policy under each MDP subtask. The quality of the proposed scheme is experimentally evaluated. The result of experiments implies that it can acquire a deterministic policy with sufficiently high success rate, even if the given task is POMDP with probabilistic state transitions.
論文電子ファイル Not available.


[1-54]  Hiroshi Osada and Satoshi Fujita, ``CHQ: a Multi-Agent Reinforcement Learning Scheme for Partially Observable Markov Decision Processes,'' In Proc. IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'04), pp. 17-23, September 2004. (Beijing)

@inproceedings{1_54,
    author = {Hiroshi Osada and Satoshi Fujita},
    author_e = {},
    editor = {},
    editor_e = {},
    title = {{CHQ}: A Multi-Agent Reinforcement Learning Scheme for Partially
    Observable Markov Decision Processes},
    title_e = {},
    booktitle = {Proc. IEEE/WIC/ACM International Conference on Intelligent
    Agent Technology (IAT'04)},
    booktitle_e = {},
    volume = {},
    number = {},
    pages = {17-23},
    organization = {},
    publisher = {},
    publisher_e = {},
    address = {},
    month = {September},
    year = {2004},
    note = {Beijing},
    annote = {DOI: 10.1109/IAT.2004.1342918, Acceptance rate: 44/266 = 17%}
}

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