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Volumn 1, Issue , 2010, Pages 75-82

High-level reinforcement learning in strategy games

Author keywords

Reinforcement Learning; Video games; Virtual agents

Indexed keywords

AUTONOMOUS AGENTS; INTELLIGENT AGENTS; MULTI AGENT SYSTEMS; REINFORCEMENT LEARNING;

EID: 84865771838     PISSN: 15488403     EISSN: 15582914     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (40)

References (15)
  • 8
    • 0030050933 scopus 로고
    • Multiagent reinforcement learning in the iterated prisoner's dilemma
    • T. W. Sandholm and R. H. Crites. Multiagent reinforcement learning in the iterated prisoner's dilemma. Biosystems, 37:147-166, 1995.
    • (1995) Biosystems , vol.37 , pp. 147-166
    • Sandholm, T.W.1    Crites, R.H.2
  • 9
    • 58149326017 scopus 로고
    • On players' models of other players: Theory and experimental evidence
    • D. O. Stahl and P. W. Wilson. On players' models of other players: Theory and experimental evidence. Games and Economic Behavior, 10:218-254, 1995.
    • (1995) Games and Economic Behavior , vol.10 , pp. 218-254
    • Stahl, D.O.1    Wilson, P.W.2
  • 14
    • 34249833101 scopus 로고
    • Technical note: Q-learning
    • C. J. C. H. Watkins and P. Dayan. Technical note: Q-learning. Machine Learning, 8(3-4):279-292, 1992.
    • (1992) Machine Learning , vol.8 , Issue.3-4 , pp. 279-292
    • Watkins, C.J.C.H.1    Dayan, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.