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Volumn , Issue , 2004, Pages

Extending Q-learning to general adaptive multi-agent systems

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; INFERENCE ENGINES;

EID: 84898941549     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (130)

References (14)
  • 1
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    • Convergence problems of general-sum multiagent reinforcement learning
    • M. Bowling. Convergence problems of general-sum multiagent reinforcement learning. In Proceedings of ICML-00, pages 89-94, 2000.
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    • Bowling, M.1
  • 2
    • 0036531878 scopus 로고    scopus 로고
    • Multiagent learning using a variable learning rate
    • M. Bowling and M. Veloso. Multiagent learning using a variable learning rate. Artificial Intelligence, 136:215-250, 2002.
    • (2002) Artificial Intelligence , vol.136 , pp. 215-250
    • Bowling, M.1    Veloso, M.2
  • 3
    • 84898960502 scopus 로고    scopus 로고
    • Playing is believing: The role of beliefs in multi-agent learning
    • MIT Press
    • Y.-H. Chang and L. P. Kaelbling. Playing is believing: The role of beliefs in multi-agent learning. In Proceedings of NIPS-2001. MIT Press, 2002.
    • (2002) Proceedings of NIPS-2001
    • Chang, Y.-H.1    Kaelbling, L.P.2
  • 4
    • 78149326576 scopus 로고    scopus 로고
    • Multiplicative adjustment of class probability: Educating naive bayes
    • IBM Research
    • S. J. Hong, J. Hosking, and R. Natarajan. Multiplicative adjustment of class probability: Educating naive Bayes. Technical Report RC-22393, IBM Research, 2002.
    • (2002) Technical Report RC-22393
    • Hong, S.J.1    Hosking, J.2    Natarajan, R.3
  • 5
    • 0000929496 scopus 로고    scopus 로고
    • Multiagent reinforcement learning: Theoretical framework and an algorithm
    • Morgan Kaufmann
    • J. Hu and M. P. Wellman. Multiagent reinforcement learning: Theoretical framework and an algorithm. In Proceedings of ICML-98, pages 242-250. Morgan Kaufmann, 1998.
    • (1998) Proceedings of ICML-98 , pp. 242-250
    • Hu, J.1    Wellman, M.P.2
  • 6
    • 31144466138 scopus 로고    scopus 로고
    • Efficient nash computation in large population games with bounded influence
    • M. Kearns and Y. Mansour. Efficient Nash computation in large population games with bounded influence. In Proceedings of UAI-02, pages 259-266, 2002.
    • (2002) Proceedings of UAI-02 , pp. 259-266
    • Kearns, M.1    Mansour, Y.2
  • 7
    • 85149834820 scopus 로고
    • Markov games as a framework for multi-agent reinforcement learning
    • Morgan Kaufmann
    • M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Proceedings of ICML-94, pages 157-163. Morgan Kaufmann, 1994.
    • (1994) Proceedings of ICML-94 , pp. 157-163
    • Littman, M.L.1
  • 8
    • 0242466944 scopus 로고    scopus 로고
    • Friend-or-foe Q-learning in general-sum games
    • Morgan Kaufmann
    • M. L. Littman. Friend-or-Foe Q-learning in general-sum games. In Proceedings of ICML-01. Morgan Kaufmann, 2001.
    • (2001) Proceedings of ICML-01
    • Littman, M.L.1
  • 9
    • 0039225090 scopus 로고    scopus 로고
    • A convergent reinforcement learning algorithm in the continuous case based on a finite difference method
    • Morgan Kaufman
    • R. Munos. A convergent reinforcement learning algorithm in the continuous case based on a finite difference method. In Proceedings of IJCAI-97, pages 826-831. Morgan Kaufman, 1997.
    • (1997) Proceedings of IJCAI-97 , pp. 826-831
    • Munos, R.1
  • 10
    • 0001644761 scopus 로고    scopus 로고
    • Nash convergence of gradient dynamics in general-sum games
    • Morgan Kaufman
    • S. Singh, M. Kearns, and Y. Mansour. Nash convergence of gradient dynamics in general-sum games. In Proceedings of UAI-2000, pages 541-548. Morgan Kaufman, 2000.
    • (2000) Proceedings of UAI-2000 , pp. 541-548
    • Singh, S.1    Kearns, M.2    Mansour, Y.3
  • 11
    • 0001898381 scopus 로고    scopus 로고
    • Practical reinforcement learning in continuous spaces
    • W. D. Smart and L. P. Kaelbling. Practical reinforcement learning in continuous spaces. In Proceedings of ICML-00, pages 903-910, 2000.
    • (2000) Proceedings of ICML-00 , pp. 903-910
    • Smart, W.D.1    Kaelbling, L.P.2
  • 12
    • 0031636218 scopus 로고    scopus 로고
    • Tree based discretization for continuous state space reinforcement learning
    • W. T. B. Uther and M. M. Veloso. Tree based discretization for continuous state space reinforcement learning. In Proceedings of AAAI-98, pages 769-774, 1998.
    • (1998) Proceedings of AAAI-98 , pp. 769-774
    • Uther, W.T.B.1    Veloso, M.M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.