메뉴 건너뛰기




Volumn 15, Issue 3, 2007, Pages 281-312

Generalized multiagent learning with performance bound

Author keywords

Game theory; Multiagent reinforcement learning

Indexed keywords


EID: 35248823118     PISSN: 13872532     EISSN: 15737454     Source Type: Journal    
DOI: 10.1007/s10458-007-9013-x     Document Type: Article
Times cited : (17)

References (36)
  • 3
    • 84899027977 scopus 로고    scopus 로고
    • Convergence and no-regret in multiagent learning
    • Bowling, M. (2005). Convergence and no-regret in multiagent learning. In Proceedings of NIPS 2004/5.
    • (2005) Proceedings of NIPS 2004/5
    • Bowling, M.1
  • 5
    • 0036531878 scopus 로고    scopus 로고
    • Multiagent learning using a variable learning rate
    • Bowling M., Veloso M. (2002). Multiagent learning using a variable learning rate. Artificial Intelligence 136: 215-250
    • (2002) Artificial Intelligence , vol.136 , pp. 215-250
    • Bowling, M.1    Veloso, M.2
  • 6
    • 0041965975 scopus 로고    scopus 로고
    • R-max - A general polynomial time algorithm for near-optimal reinforcement learning
    • Brafman R.I., Tennenholtz M. (2002). R-max - A general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research 3: 213-231
    • (2002) Journal of Machine Learning Research , vol.3 , pp. 213-231
    • Brafman, R.I.1    Tennenholtz, M.2
  • 8
    • 1942421183 scopus 로고    scopus 로고
    • AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents
    • Conitzer, V., & Sandholm, T. (2003). AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents. In Proceedings of the twentieth international conference on machine learning.
    • (2003) Proceedings of the Twentieth International Conference on Machine Learning
    • Conitzer, V.1    Sandholm, T.2
  • 11
    • 0002267135 scopus 로고    scopus 로고
    • Adaptive game playing using multiplicative weights
    • Freund Y., Schapire R.E. (1999). Adaptive game playing using multiplicative weights. Games and Economic Behavior 29: 79-103
    • (1999) Games and Economic Behavior , vol.29 , pp. 79-103
    • Freund, Y.1    Schapire, R.E.2
  • 15
    • 2942744741 scopus 로고    scopus 로고
    • Uncoupled dynamics do not lead to nash equilibrium
    • 3
    • Hart S., Mas-Colell A. (2003) Uncoupled dynamics do not lead to nash equilibrium. American Economic Review 93(3): 1830-1836
    • (2003) American Economic Review , vol.93 , pp. 1830-1836
    • Hart, S.1    Mas-Colell, A.2
  • 19
    • 85149834820 scopus 로고
    • Markov games as a framework for multi-agent reinforcement learning
    • San Mateo, CA: Morgan Kaufmann
    • Littman, M. L. (1994). Markov games as a framework for multi-agent reinforcement learning. In Proceedings of the eleventh international conference on machine learning, (pp. 157-163). San Mateo, CA: Morgan Kaufmann.
    • (1994) Proceedings of the Eleventh International Conference on Machine Learning , pp. 157-163
    • Littman, M.L.1
  • 22
    • 0001730497 scopus 로고
    • Non-cooperative games
    • Nash J.F. (1951). Non-cooperative games. Annals of Mathematics 54: 286-295
    • (1951) Annals of Mathematics , vol.54 , pp. 286-295
    • Nash, J.F.1
  • 23
    • 0027336968 scopus 로고
    • A strategy of win-stay, lose-shift that outperforms tit-for-tat in the prisoner's dilemma game
    • Nowak M., Sigmund K. (1993). A strategy of win-stay, lose-shift that outperforms tit-for-tat in the prisoner's dilemma game. Nature 364: 56-58
    • (1993) Nature , vol.364 , pp. 56-58
    • Nowak, M.1    Sigmund, K.2
  • 24
    • 0004260006 scopus 로고
    • Academic Press UK
    • Owen G. (1995). Game Theory. UK, Academic Press
    • (1995) Game Theory
    • Owen, G.1
  • 26
    • 84898936075 scopus 로고    scopus 로고
    • New criteria and a new algorithm for learning in multi-agent systems
    • Powers, R., & Shoham, Y. (2005). New criteria and a new algorithm for learning in multi-agent systems. In Proceedings of NIPS 2004/5.
    • (2005) Proceedings of NIPS 2004/5
    • Powers, R.1    Shoham, Y.2
  • 27
    • 84949966897 scopus 로고    scopus 로고
    • On multiagent Q-learning in a semi-competitive domain
    • G. Weiß & S. Sen, (Eds.) Springer-Verlag
    • Sandholm T., Crites R. (1996). On multiagent Q-learning in a semi-competitive domain. In G. Weiß & S. Sen, (Eds.) Adaptation and learning in multi-agent systems. pp. 191-205, Springer-Verlag.
    • (1996) Adaptation and Learning in Multi-agent Systems , pp. 191-205
    • Sandholm, T.1    Crites, R.2
  • 28
    • 0028555752 scopus 로고
    • Learning to coordinate without sharing information
    • Menlo Park, CA: AAAI Press/MIT Press. (Also published in READINGS in AGENTS, Michael Huhns, N, and Munindar Singh (Editors), p. 509-514, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1998.)
    • Sen, S., Sekaran, M., & Hale, J. (1994). Learning to coordinate without sharing information. In National conference on artificial intelligence, p. 426-431, Menlo Park, CA: AAAI Press/MIT Press. (Also published in READINGS in AGENTS, Michael Huhns, N, and Munindar Singh (Editors), p. 509-514, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1998.).
    • (1994) National Conference on Artificial Intelligence , pp. 426-431
    • Sen, S.1    Sekaran, M.2    Hale, J.3
  • 33
    • 84898941549 scopus 로고    scopus 로고
    • Extending Q-learning to general adaptive multi-agent systems
    • S. Thrun, L. Saul, & B. Schölkopf, (Eds) Cambridge, MA: MIT Press
    • Tesauro, G. (2004). Extending Q-learning to general adaptive multi-agent systems. In S. Thrun, L. Saul, & B. Schölkopf, (Eds), Advances in neural information processing systems Vol. 16. Cambridge, MA: MIT Press.
    • (2004) Advances in Neural Information Processing Systems , vol.16
    • Tesauro, G.1
  • 34
    • 67649405225 scopus 로고    scopus 로고
    • Reinforcement learning to play an optimal nash equilibrium in team markov games
    • NIPS
    • Wang, X., & Sandholm, T. (2002). Reinforcement learning to play an optimal nash equilibrium in team markov games. In Advances in neural information processing systems 15, NIPS.
    • (2002) Advances in Neural Information Processing Systems , vol.15
    • Wang, X.1    Sandholm, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.