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Volumn , Issue , 2005, Pages 121-128

Hedged learning: Regret-minimization with learning experts

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; LEARNING ALGORITHMS; MATHEMATICAL MODELS; MULTI AGENT SYSTEMS; OPTIMIZATION;

EID: 31844456904     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (13)

References (10)
  • 2
    • 4544369813 scopus 로고    scopus 로고
    • Playing is believing: The role of beliefs in multi-agent learning
    • Chang, Y., & Kaelbling, L. P. (2001). Playing is believing: The role of beliefs in multi-agent learning. NIPS.
    • (2001) NIPS
    • Chang, Y.1    Kaelbling, L.P.2
  • 3
    • 33845304828 scopus 로고    scopus 로고
    • How to combine expert (or novice) advice when actions impact the environment
    • de Farias, D. P., & Meggido, N. (2004). How to combine expert (or novice) advice when actions impact the environment. Proceedings of NIPS.
    • (2004) Proceedings of NIPS
    • Farias, D.P.1    Meggido, N.2
  • 4
    • 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, 89, 79-103.
    • (1999) Games and Economic Behavior , vol.89 , pp. 79-103
    • Freund, Y.1    Schapire, R.E.2
  • 6
    • 0000929496 scopus 로고    scopus 로고
    • Multiagent reinforcement learning: Theoretical framework and an algorithm
    • Hu, J., & Wellman, M. P. (1998). Multiagent reinforcement learning: Theoretical framework and an algorithm. Proceedings of the 15th ICML.
    • (1998) Proceedings of the 15th ICML
    • Hu, J.1    Wellman, M.P.2
  • 7
    • 0012257655 scopus 로고    scopus 로고
    • Near-optimal reinforcement learning in polynomial time
    • Kearns, M., & Singh, S. (1998). Near-optimal reinforcement learning in polynomial time. ICML.
    • (1998) ICML
    • Kearns, M.1    Singh, S.2
  • 8
    • 85149834820 scopus 로고
    • Markov games as a framework for multi-agent reinforcement learning
    • Littman, M. L. (1994). Markov games as a framework for multi-agent reinforcement learning. ICML.
    • (1994) ICML
    • Littman, M.L.1
  • 9
    • 31844439377 scopus 로고    scopus 로고
    • Adaptive strategies and regret minimization in arbitrarily varying Markov environments
    • Mannor, S., & Shimkin, N. (2001), Adaptive strategies and regret minimization in arbitrarily varying Markov environments. Proc. of 14th COLT.
    • (2001) Proc. of 14th COLT
    • Mannor, S.1    Shimkin, N.2
  • 10
    • 0030306234 scopus 로고    scopus 로고
    • Non-computable strategies and discounted repeated games
    • Nachbar, J., & Zame, W. (1996). Non-computable strategies and discounted repeated games. Economic Theory.
    • (1996) Economic Theory
    • Nachbar, J.1    Zame, W.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.