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Volumn 15, Issue 1, 2011, Pages 55-64

The world of independent learners is not markovian

Author keywords

machine learning; Multi agent system; reinforcement learning

Indexed keywords

FORMAL CONCEPTS; LEARNING AGENTS; LEARNING METHODS; LEARNING PATHS; MARKOVIAN; NON-MARKOVIAN; SINGLE-AGENT;

EID: 80052079894     PISSN: 13272314     EISSN: 18758827     Source Type: Journal    
DOI: 10.3233/KES-2010-0206     Document Type: Article
Times cited : (137)

References (41)
  • 2
    • 18744371204 scopus 로고    scopus 로고
    • Reinforcement learning in markovian evolutionary games
    • V. S. Borkar, Reinforcement learning in markovian evolutionary games, Advances in Complex Systems 5(1) (2002), 55-72.
    • (2002) Advances in Complex Systems , vol.5 , Issue.1 , pp. 55-72
    • Borkar, V.S.1
  • 3
    • 22944447799 scopus 로고    scopus 로고
    • PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA, May
    • M. Bowling, Multiagent Learning in the Presence of Agents with Limitations, PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA, May 2003.
    • (2003) Multiagent Learning in the Presence of Agents with Limitations
    • Bowling, M.1
  • 4
    • 0036531878 scopus 로고    scopus 로고
    • Multiagent learning using a variable learning rate
    • DOI 10.1016/S0004-3702(02)00121-2, PII S0004370202001212
    • M. Bowling and M. Veloso, Multiagent learning using a variable learning rate, Artificial Intelligence 136(2002), 215-250. (Pubitemid 34232184)
    • (2002) Artificial Intelligence , vol.136 , Issue.2 , pp. 215-250
    • Bowling, M.1    Veloso, M.2
  • 5
    • 0003863106 scopus 로고    scopus 로고
    • An analysis of stochastic game theory for multiagent reinforcement learning
    • Computer Science Department, Carnegie Mellon University
    • M. Bowling and M. M. Veloso, An analysis of stochastic game theory for multiagent reinforcement learning, Technical Report CMU-CS-00-165, Computer Science Department, Carnegie Mellon University, 2000.
    • (2000) Technical Report CMU-CS-00-165
    • Bowling, M.1    Veloso, M.M.2
  • 10
    • 80052097767 scopus 로고    scopus 로고
    • Decentralized reinforcement learning for the online optimization of distributed systems
    • C. Weber, M. Elshaw and N. M. Mayer, eds, I-TECH Education and Publishing
    • J. Dowling and S. Haridi, Decentralized reinforcement learning for the online optimization of distributed systems, in: Reinforcement Learning: Theory and Applications, C. Weber, M. Elshaw and N. M. Mayer, eds, I-TECH Education and Publishing, 2008, pp. 143-166.
    • (2008) Reinforcement Learning: Theory and Applications , pp. 143-166
    • Dowling, J.1    Haridi, S.2
  • 13
    • 33748543203 scopus 로고    scopus 로고
    • Collaborative multiagent reinforcement learning by payoff propagation
    • J. R. Kok and N. Vlassis, Collaborative multiagent reinforcement learning by payoff propagation, Journal of Machine Learning Research 7(2006), 1789-1828. (Pubitemid 44373693)
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1789-1828
    • Kok, J.R.1    Vlassis, N.2
  • 14
    • 0012286079 scopus 로고    scopus 로고
    • An algorithm for distributed reinforcement learning in cooperative multi-agent systems
    • Morgan Kaufmann
    • M. Lauer and M. Riedmiller, An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems, In Proc. of the Int. Conf. on Machine Learning, pages 535-542. Morgan Kaufmann, 2000.
    • (2000) Proc. of the Int. Conf. on Machine Learning , pp. 535-542
    • Lauer, M.1    Riedmiller, M.2
  • 16
    • 0001547175 scopus 로고    scopus 로고
    • Value-function reinforcement learning in Markov games
    • PII S1389041701000158
    • M. L. Littman, Value-function reinforcement learning in markov games, Journal of Cognitive Systems Research 2(1) (2001), 55-66. (Pubitemid 33718550)
    • (2001) Cognitive Systems Research , vol.2 , Issue.1 , pp. 55-66
    • Littman, M.L.1
  • 17
    • 51349117828 scopus 로고    scopus 로고
    • Hysteretic Q-learning: An algorithm for decentralized reinforcement learning in cooperative multi-agent teams
    • San Diego, CA, USA, Oct. 29-Nov. 2
    • L. Matignon, G. J. Laurent and N. Le Fort-Piat, Hysteretic Q-Learning: An Algorithm for Decentralized Reinforcement Learning in Cooperative Multi-Agent Teams, In Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems, pages 64-69, San Diego, CA, USA, Oct. 29-Nov. 2 2007.
    • (2007) Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems , pp. 64-69
    • Matignon, L.1    Laurent, G.J.2    Le Fort-Piat, N.3
  • 18
    • 77955659466 scopus 로고    scopus 로고
    • Coordination of independent learners in cooperative markov games
    • Institut FEMTO-ST/UFC-ENSMM-UTBMCNRS, Besançon, France, March
    • L. Matignon, G. J. Laurent and N. Le Fort-Piat, Coordination of independent learners in cooperative markov games, Technical Report RR-2009-01, Institut FEMTO-ST/UFC-ENSMM-UTBMCNRS, Besançon, France, March 2009. http://hal.archives-ouvertes.fr/hal-00370889/fr/.
    • (2009) Technical Report RR-2009-01
    • Matignon, L.1    Laurent, G.J.2    Le Fort-Piat, N.3
  • 19
    • 77955654600 scopus 로고    scopus 로고
    • Designing decentralized controllers for distributed-airjet mems-based micromanipulators by reinforcement learning
    • L. Matignon, G. J. Laurent, N. L. Fort-Piat and Y.-A. Chapuis, Designing decentralized controllers for distributed-airjet mems-based micromanipulators by reinforcement learning, Journal of Intelligent and Robotic Systems 59(2) (2010), 145-166.
    • (2010) Journal of Intelligent and Robotic Systems , vol.59 , Issue.2 , pp. 145-166
    • Matignon, L.1    Laurent, G.J.2    Fort-Piat, N.L.3    Chapuis, Y.-A.4
  • 24
    • 26444601262 scopus 로고    scopus 로고
    • Cooperative multi-agent learning: The state of the art
    • DOI 10.1007/s10458-005-2631-2
    • L. Panait and S. Luke, Cooperative multi-agent learning: The state of the art, Autonomous Agents and Multi-Agent Systems 11(3) (2005), 387-434. (Pubitemid 41425094)
    • (2005) Autonomous Agents and Multi-Agent Systems , vol.11 , Issue.3 , pp. 387-434
    • Panait, L.1    Luke, S.2
  • 25
    • 41549123971 scopus 로고    scopus 로고
    • Theoretical advantages of lenient learners: An evolutionary game theoretic perspective
    • L. Panait, K. Tuyls and S. Luke, Theoretical advantages of lenient learners: An evolutionary game theoretic perspective, Journal of Machine Learning Research 9(2008), 423-457. (Pubitemid 351469016)
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 423-457
    • Panait, L.1    Tuyls, K.2    Luke, S.3
  • 26
    • 34249045960 scopus 로고    scopus 로고
    • Perspectives on multiagent learning
    • T. Sandholm, Perspectives on multiagent learning, Artificial Intelligence 171(2007), 382-392.
    • (2007) Artificial Intelligence , vol.171 , pp. 382-392
    • Sandholm, T.1
  • 28
    • 0000392613 scopus 로고
    • Reprinted in Kuhn, 1997
    • L. S. Shapley, Stochastic games, PNAS 39(1953), 1095-1100, Reprinted in (Kuhn, 1997).
    • (1953) Stochastic Games, PNAS , vol.39 , pp. 1095-1100
    • Shapley, L.S.1
  • 30
    • 85152198941 scopus 로고
    • Multiagent reinforcement learning: Independent vs. cooperative agents
    • M. Tan, Multiagent Reinforcement Learning: Independent vs. Cooperative Agents, In Proc. of the Int. Conf. on Machine Learning, pages 330-337, 1993.
    • (1993) Proc. of the Int. Conf. on Machine Learning , pp. 330-337
    • Tan, M.1
  • 31
    • 33847379922 scopus 로고    scopus 로고
    • Reinforcement learning in autonomic computing: A manifesto and case studies
    • DOI 10.1109/MIC.2007.21
    • G. Tesauro, Reinforcement learning in autonomic computing: A manifesto and case studies, IEEE Internet Computing 11(2) (2007), 22-30. (Pubitemid 46335538)
    • (2007) IEEE Internet Computing , vol.11 , Issue.1 , pp. 22-30
    • Tesauro, G.1
  • 32
    • 31344450384 scopus 로고    scopus 로고
    • An evolutionary dynamical analysis of multi-agent learning in iterated games
    • DOI 10.1007/s10458-005-3783-9
    • K. Tuyls, P. Jan, T. Hoen and B. Vanschoenwinkel, An evolutionary dynamical analysis of multi-agent learning in iterated games, Autonomous Agents and Multi-Agent Systems 12(2006), 115-153. (Pubitemid 43146342)
    • (2006) Autonomous Agents and Multi-Agent Systems , vol.12 , Issue.1 , pp. 115-153
    • Tuyls, K.1    T Hoen, P.J.2    Vanschoenwinkel, B.3
  • 36
    • 43549119106 scopus 로고    scopus 로고
    • A machine-learning approach to multi-robot coordination
    • DOI 10.1016/j.engappai.2007.05.006, PII S0952197607000693
    • Y. Wang and C. W. de Silva, A machine-learning approach to multi-robot coordination, Engineering Applications of Artificial Intelligence 21(3) (2008), 470-484. (Pubitemid 351680683)
    • (2008) Engineering Applications of Artificial Intelligence , vol.21 , Issue.3 , pp. 470-484
    • Wang, Y.1    De Silva, C.W.2
  • 39
    • 33746826183 scopus 로고    scopus 로고
    • Multiagent reinforcement learning for multirobot systems: A survey
    • Department of Computer Science, University of Essex
    • E. Yang and D. Gu, Multiagent reinforcement learning for multirobot systems: A survey. Technical report, Department of Computer Science, University of Essex, 2004.
    • (2004) Technical Report
    • Yang, E.1    Gu, D.2
  • 40
    • 34249001282 scopus 로고    scopus 로고
    • The possible and the impossible in multi-agent learning
    • DOI 10.1016/j.artint.2006.10.015, PII S0004370207000367, Foundations of Multi-Agent Learning
    • H. P. Young, The possible and the impossible in multi-agent learning, Artificial Intelligence 171(2007), 429-433. (Pubitemid 46802420)
    • (2007) Artificial Intelligence , vol.171 , Issue.7 , pp. 429-433
    • Young, H.P.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.