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Volumn , Issue , 2001, Pages 825-830

Fast concurrent reinforcement learners

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE TO EQUILIBRIUM; NONSTATIONARY; RATE OF CONVERGENCE; SINGLE-AGENT;

EID: 84880876200     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (30)

References (14)
  • 1
    • 85153940465 scopus 로고
    • Generalization in reinforcement learning: Safely approximating the value function
    • J.A. Boyan and A.W. Moore. Generalization in reinforcement learning: Safely approximating the value function. In Advances in Neural Information Processing Systems 7, pages 369-376, 1995.
    • (1995) Advances in Neural Information Processing Systems 7 , pp. 369-376
    • Boyan, J.A.1    Moore, A.W.2
  • 2
    • 0000929496 scopus 로고    scopus 로고
    • Multiagent reinforcement learning: Theoretical framework and an algorithm
    • San Francisco, CA, Morgan Kaufmann
    • J. Hu and M. P. Wellman. Multiagent reinforcement learning: Theoretical framework and an algorithm. In Proc. of the 15th Int. Conf. on Machine Learning (ML'98), pages 242-250, San Francisco, CA, 1998. Morgan Kaufmann.
    • (1998) Proc. of the 15th Int. Conf. on Machine Learning (ML'98) , pp. 242-250
    • Hu, J.1    Wellman, M.P.2
  • 3
    • 85149834820 scopus 로고
    • Markov games as a framework for multi-agent reinforcement learning
    • San Mateo, CA, Morgan Kaufmann
    • M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Proc. of the 11th Int. Conf. on Machine Learning, pages 157-163, San Mateo, CA, 1994. Morgan Kaufmann.
    • (1994) Proc. of the 11th Int. Conf. on Machine Learning , pp. 157-163
    • Littman, M.L.1
  • 5
    • 0001730497 scopus 로고
    • Non-cooperative games
    • John F. Nash. Non-cooperative games. Annals of Mathematics, 54:286-295, 1951.
    • (1951) Annals of Mathematics , vol.54 , pp. 286-295
    • Nash, J.F.1
  • 6
    • 0000955979 scopus 로고    scopus 로고
    • Incremental multi-step Q-learning
    • J. Peng and R. Williams. Incremental multi-step Q-learning. Machine Learning, 22:283-290, 1996. (Pubitemid 126724369)
    • (1996) Machine Learning , vol.22 , Issue.1-3 , pp. 283-290
    • Peng, J.1    Williams, R.J.2
  • 8
    • 84949966897 scopus 로고    scopus 로고
    • On multiagent Q-learning in a semi-competitive domain
    • G. Weiß and S. Sen, editors, Springer-Verlag
    • T. Sandholm and R. Crites. On multiagent Q-learning in a semi-competitive domain. In G. Weiß and S. Sen, editors, Adaptation and Learning in Multi-Agent Systems, pages 191-205. Springer-Verlag, 1996.
    • (1996) Adaptation and Learning in Multi-Agent Systems , pp. 191-205
    • Sandholm, T.1    Crites, R.2
  • 10
    • 0000723997 scopus 로고    scopus 로고
    • Generalization in reinforcement learning: Successful examples using sparse coarse coding
    • R. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. In Advances in Neural Information Processing Systems 8, 1996.
    • (1996) Advances in Neural Information Processing Systems 8
    • Sutton, R.1
  • 11
    • 30844447222 scopus 로고    scopus 로고
    • A unified analysis of value-function-based reinforcement-learning algorithms
    • submitted
    • Csaba Szepesvári and M.L. Littman. A unified analysis of value-function-based reinforcement-learning algorithms. In Neural Computation, 1997. submitted.
    • (1997) Neural Computation
    • Szepesvári, C.1    Littman, M.L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.