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Volumn , Issue , 2013, Pages 31-38

Exponential moving average Q-learning algorithm

Author keywords

[No Author keywords available]

Indexed keywords

EXPONENTIAL MOVING AVERAGES; MULTI-AGENT REINFORCEMENT LEARNING; OPTIMAL POLICIES; POLICY ITERATION; Q-LEARNING AGENTS; Q-LEARNING ALGORITHMS; STOCHASTIC GAME;

EID: 84891544020     PISSN: 23251824     EISSN: 23251867     Source Type: Conference Proceeding    
DOI: 10.1109/ADPRL.2013.6614986     Document Type: Conference Paper
Times cited : (16)

References (17)
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    • Bowling, M.1    Veloso, M.2
  • 5
    • 40949147745 scopus 로고    scopus 로고
    • A comprehensive survey of multiagent reinforcement learning
    • L. Buşoniu and R. Babu?ska and B. D. Schutter, A comprehensive survey of multiagent reinforcement learning, IEEE Trans Syst Man Cybern C, 38(2):156-172, 2008.
    • (2008) IEEE Trans Syst Man Cybern C , vol.38 , Issue.2 , pp. 156-172
    • Busoniu, L.1    Babuska, R.2    Schutter, B.D.3
  • 6
    • 70350699723 scopus 로고    scopus 로고
    • A multiagent reinforcement learning algorithm with non-linear dynamics
    • S. Abdallah and V. Lesser, A multiagent reinforcement learning algorithm with non-linear dynamics, Journal of Artificial Intelligence Research 33, pp. 521-549, 2008.
    • (2008) Journal of Artificial Intelligence Research , vol.33 , pp. 521-549
    • Abdallah, S.1    Lesser, V.2
  • 7
    • 70350566689 scopus 로고    scopus 로고
    • Effective learning in the presence of adaptive counterparts
    • A. Burkov and B. Chaib-draa, Effective learning in the presence of adaptive counterparts, Journal of Algorithms, 64(4):127-138, 2009.
    • (2009) Journal of Algorithms , vol.64 , Issue.4 , pp. 127-138
    • Burkov, A.1    Chaib-Draa, B.2
  • 8
    • 34147159616 scopus 로고    scopus 로고
    • Awesome: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents
    • V. Conitzer and T. Sandholm, Awesome: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents, Machine Learning, 67(1-2), pp. 23-43, 2007.
    • (2007) Machine Learning , vol.67 , Issue.1-2 , pp. 23-43
    • Conitzer, V.1    Sandholm, T.2
  • 11
    • 4644369748 scopus 로고    scopus 로고
    • Nash q-learning for general-sum stochastic games
    • J. Hu and M. P. Wellman, Nash q-learning for general-sum stochastic games, Journal of Machine Learning Research, 4:1039-1069, 2003.
    • (2003) Journal of Machine Learning Research , vol.4 , pp. 1039-1069
    • Hu, J.1    Wellman, M.P.2
  • 16
    • 84898941549 scopus 로고    scopus 로고
    • Extending q-learning to general adaptive multi-agent systems
    • G. Tesauro, Extending q-learning to general adaptive multi-agent systems, In Advances in Neural Information Processing Systems 16, pp. 215-250, 2004.
    • (2004) Advances in Neural Information Processing Systems , vol.16 , pp. 215-250
    • Tesauro, G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.