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Volumn 5163 LNCS, Issue PART 1, 2008, Pages 357-366

Multigrid reinforcement learning with reward shaping

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND KNOWLEDGE; CONVERGENCE RATES; DISCRETISATION; LEARNING AGENTS; LOWER RESOLUTION; MULTI-GRID; NOVEL ALGORITHM; POTENTIAL FUNCTION; Q-FUNCTIONS; REINFORCEMENT LEARNING AGENT; STATE SPACE; TEMPORAL DIFFERENCE LEARNING;

EID: 58849111871     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87536-9_37     Document Type: Conference Paper
Times cited : (28)

References (18)
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    • Decision-theoretic planning: Structural assumptions and computational leverage
    • Boutilier, C., Dean, T., Hanks, S.: Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research 11, 1-94 (1999)
    • (1999) Journal of Artificial Intelligence Research , vol.11 , pp. 1-94
    • Boutilier, C.1    Dean, T.2    Hanks, S.3
  • 6
    • 0026206780 scopus 로고
    • An optimal one-way multigrid algorithm for discretetime stochastic control
    • Chow, C.S., Tsitsiklis, J.N.: An optimal one-way multigrid algorithm for discretetime stochastic control. IEEE Transactions on Automatic Control 36(8), 898-914 (1991)
    • (1991) IEEE Transactions on Automatic Control , vol.36 , Issue.8 , pp. 898-914
    • Chow, C.S.1    Tsitsiklis, J.N.2
  • 7
    • 0042353224 scopus 로고
    • Multigrid Q-learning
    • Technical Report CS-94-121, Colorado State University
    • Anderson, C., Crawford-Hines, S.: Multigrid Q-learning. Technical Report CS-94-121, Colorado State University (1994)
    • (1994)
    • Anderson, C.1    Crawford-Hines, S.2
  • 8
    • 0033170372 scopus 로고    scopus 로고
    • Between MDPs and Semi-MDPs: A framework for temporal abstraction in reinforcement learning
    • Sutton, R.S., Precup, D., Singh, S.P.: Between MDPs and Semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence 112(1-2), 181-211 (1999)
    • (1999) Artificial Intelligence , vol.112 , Issue.1-2 , pp. 181-211
    • Sutton, R.S.1    Precup, D.2    Singh, S.P.3
  • 10
    • 34250717446 scopus 로고    scopus 로고
    • Epshteyn, A., De.Jong, G.: Qualitative reinforcement learning. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 305-312 (2006)
    • Epshteyn, A., De.Jong, G.: Qualitative reinforcement learning. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 305-312 (2006)
  • 11
    • 0036832953 scopus 로고    scopus 로고
    • Variable resolution discretization in optimal control
    • Munos, R., Moore, A.: Variable resolution discretization in optimal control. Machine Learning 49(2-3), 291-323 (2002)
    • (2002) Machine Learning , vol.49 , Issue.2-3 , pp. 291-323
    • Munos, R.1    Moore, A.2
  • 17
    • 0002278788 scopus 로고    scopus 로고
    • Hierarchical reinforcement learning with the MAXQ value function decomposition
    • Dietterich, T.G.: Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research 13, 227-303 (2000)
    • (2000) Journal of Artificial Intelligence Research , vol.13 , pp. 227-303
    • Dietterich, T.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.