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Volumn , Issue , 2005, Pages 35-42

Multiagent reinforcement learning with adaptive state focus

Author keywords

Adaptive learning; Coordination; Multiagent learning; Q learning

Indexed keywords

ADAPTIVE LEARNING; CLASS OF METHODS; COORDINATION; MULTI-AGENT LEARNING; MULTI-AGENT REINFORCEMENT LEARNING; Q-LEARNING; SINGLE-AGENT; STATE INFORMATION; STATE SPACE;

EID: 84873428767     PISSN: 15687805     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (20)

References (9)
  • 1
    • 0036531878 scopus 로고    scopus 로고
    • Multiagent learning using a variable learning rate
    • Michael Bowling and Manuela Veloso. Multiagent learning using a variable learning rate. Artificial Intelligence, 136(2):215-250, 2002.
    • (2002) Artificial Intelligence , vol.136 , Issue.2 , pp. 215-250
    • Bowling, M.1    Veloso, M.2
  • 4
    • 4644369748 scopus 로고    scopus 로고
    • Nash Q-learning for general-sum stochastic games
    • Junling Hu and Michael 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
  • 7
    • 84898936075 scopus 로고    scopus 로고
    • New criteria and a new algorithm for learning in multi-agent systems
    • Cambridge, MA
    • Rob Powers and Yoav Shoham. New criteria and a new algorithm for learning in multi-agent systems. In Advances in Neural Information Processing Systems, number 17, pages 1089-1096. Cambridge, MA, 2005.
    • (2005) Advances in Neural Information Processing Systems , Issue.17 , pp. 1089-1096
    • Powers, R.1    Shoham, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.