메뉴 건너뛰기




Volumn , Issue , 2007, Pages 780-785

Predicting and preventing coordination problems in cooperative Q-learning systems

Author keywords

[No Author keywords available]

Indexed keywords

CONCEPTUAL FRAMEWORKS; COORDINATION PROBLEMS; DESIGN TOOL; MULTI-AGENT LEARNING; MULTI-AGENT SETTING; OPTIMAL SYSTEM PERFORMANCE; Q-LEARNING;

EID: 84880861539     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (61)

References (23)
  • 1
    • 31844436490 scopus 로고    scopus 로고
    • Convergence and noregret in multiagent learning
    • Michael Bowling. Convergence and noregret in multiagent learning. In Neural Information Processing Systems, pages 209-216, 2004.
    • (2004) Neural Information Processing Systems , pp. 209-216
    • Bowling, M.1
  • 2
    • 0030674885 scopus 로고    scopus 로고
    • Cooperative mobile robots: Antecedents and directions
    • Y. Cao, A. Fukunaga, A. Kahng, and F. Meng. Cooperative mobile robots: Antecedents and directions. Autonomous Robots, 4:1-23, 1997.
    • (1997) Autonomous Robots , vol.4 , pp. 1-23
    • Cao, Y.1    Fukunaga, A.2    Kahng, A.3    Meng, F.4
  • 5
    • 10944261341 scopus 로고    scopus 로고
    • Incremental policy learning: An equilibrium selection algorithm for reinforcement learning agents with common interests
    • Nancy Fulda and Dan Ventura. Incremental policy learning: An equilibrium selection algorithm for reinforcement learning agents with common interests. In Proceedings of the International Joint Conference on Neural Networks, pages 1121-1126, 2004.
    • (2004) Proceedings of the International Joint Conference on Neural Networks , pp. 1121-1126
    • Fulda, N.1    Ventura, D.2
  • 7
    • 4644369748 scopus 로고    scopus 로고
    • Nash q-learning for general-sum stochastic games
    • J. Hu and M. 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.2
  • 16
    • 0032359707 scopus 로고    scopus 로고
    • Individual learning of coordination knowledge
    • Sandip Sen and Mahendra Sekaran. Individual learning of coordination knowledge. JETAI, 10(3):333-356, 1998.
    • (1998) JETAI , vol.10 , Issue.3 , pp. 333-356
    • Sen, S.1    Sekaran, M.2
  • 18
    • 0034205975 scopus 로고    scopus 로고
    • Multiagent systems: A survey from a machine learning perspective
    • Peter Stone and Manuela Veloso. Multiagent systems: A survey from a machine learning perspective. Autonomous Robots, 8(3):345-383, 2000.
    • (2000) Autonomous Robots , vol.8 , Issue.3 , pp. 345-383
    • Stone, P.1    Veloso, M.2
  • 19
    • 0038637209 scopus 로고    scopus 로고
    • Multi-agent reinforcement learning: Independent vs. Cooperative learning
    • Ming Tan. Multi-agent reinforcement learning: Independent vs. cooperative learning. In Readings in Agents, pages 487-494, 1997.
    • (1997) Readings in Agents , pp. 487-494
    • Tan, M.1
  • 20
    • 0028497630 scopus 로고
    • Asynchronous stochastic approximation and q-learning
    • John N. Tsitsiklis. Asynchronous stochastic approximation and q-learning. Machine Learning, 16:185-202, 1994.
    • (1994) Machine Learning , vol.16 , pp. 185-202
    • Tsitsiklis, J.N.1
  • 21
    • 67649405225 scopus 로고    scopus 로고
    • Reinforcement learning to play an optimal nash equilibrium in team markov games
    • X. Wang and T. Sandholm. Reinforcement learning to play an optimal nash equilibrium in team markov games. In Neural Information Processing Systems, pages 1571-1578, 2002.
    • (2002) Neural Information Processing Systems , pp. 1571-1578
    • Wang, X.1    Sandholm, T.2
  • 23
    • 0001944917 scopus 로고
    • The evolution of conventions
    • H. Peyton Young. The evolution of conventions. Econometrica, 61:57-84, 1993.
    • (1993) Econometrica , vol.61 , pp. 57-84
    • Young, H.P.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.