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Volumn , Issue , 2013, Pages

Approximate dynamic programming finally performswell in the game of Tetris

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; HUMAN COMPUTER INTERACTION; OPTIMIZATION;

EID: 84898951107     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (56)

References (22)
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    • Bertsekas, D.1    Ioffe, S.2
  • 3
    • 0442320716 scopus 로고    scopus 로고
    • How to lose at tetris
    • H. Burgiel. How to Lose at Tetris. Mathematical Gazette, 81:194-200, 1997.
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    • Burgiel, H.1
  • 7
    • 33744466799 scopus 로고    scopus 로고
    • Approximate policy iteration with a policy language bias: Solving relational markov decision processes
    • A. Fern, S. Yoon, and R. Givan. Approximate Policy Iteration with a Policy Language Bias: Solving Relational Markov Decision Processes. Journal of Artificial Intelligence Research, 25:75-118, 2006.
    • (2006) Journal of Artificial Intelligence Research , vol.25 , pp. 75-118
    • Fern, A.1    Yoon, S.2    Givan, R.3
  • 10
    • 0035377566 scopus 로고    scopus 로고
    • Completely derandomized self-adaptation in evolution strategies
    • N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9:159-195, 2001.
    • (2001) Evolutionary Computation , vol.9 , pp. 159-195
    • Hansen, N.1    Ostermeier, A.2
  • 12
    • 1942420814 scopus 로고    scopus 로고
    • Reinforcement learning as classification: Leveraging modern classifiers
    • M. Lagoudakis and R. Parr. Reinforcement Learning as Classification: Leveraging Modern Classifiers. In Proceedings of ICML, pages 424-431, 2003.
    • (2003) Proceedings of ICML , pp. 424-431
    • Lagoudakis, M.1    Parr, R.2
  • 13
    • 77956523230 scopus 로고    scopus 로고
    • Analysis of a classification-based policy iteration algorithm
    • A. Lazaric, M. Ghavamzadeh, and R. Munos. Analysis of a Classification-based Policy Iteration Algorithm. In Proceedings of ICML, pages 607-614, 2010.
    • (2010) Proceedings of ICML , pp. 607-614
    • Lazaric, A.1    Ghavamzadeh, M.2    Munos, R.3
  • 14
    • 0037581251 scopus 로고
    • Modified policy iteration algorithms for discounted Markov decision problems
    • M. Puterman and M. Shin. Modified policy iteration algorithms for discounted Markov decision problems. Management Science, 24(11), 1978.
    • (1978) Management Science , vol.24 , Issue.11
    • Puterman, M.1    Shin, M.2
  • 15
    • 33845309387 scopus 로고    scopus 로고
    • The cross-entropy method: A unified approach to combinatorial optimization
    • Springer-Verlag
    • R. Rubinstein and D. Kroese. The cross-entropy method: A unified approach to combinatorial optimization, Monte-Carlo simulation, and machine learning. Springer-Verlag, 2004.
    • (2004) Monte-Carlo Simulation, and Machine Learning
    • Rubinstein, R.1    Kroese, D.2
  • 16
    • 84877625141 scopus 로고    scopus 로고
    • Performance bounds for λ-policy iteration and application to the game of tetris
    • B. Scherrer. Performance Bounds for λ-Policy Iteration and Application to the Game of Tetris. Journal of Machine Learning Research, 14:1175-1221, 2013.
    • (2013) Journal of Machine Learning Research , vol.14 , pp. 1175-1221
    • Scherrer, B.1
  • 18
    • 33845344721 scopus 로고    scopus 로고
    • Learning tetris using the noisy cross-entropy method
    • I. Szita and A. Lorincz. Learning Tetris Using the Noisy Cross-Entropy Method. Neural Computation, 18(12):2936-2941, 2006.
    • (2006) Neural Computation , vol.18 , Issue.12 , pp. 2936-2941
    • Szita, I.1    Lorincz, A.2
  • 22
    • 0029752470 scopus 로고    scopus 로고
    • Feature-based methods for large scale dynamic programming
    • J. Tsitsiklis and B Van Roy. Feature-based methods for large scale dynamic programming. Machine Learning, 22:59-94, 1996.
    • (1996) Machine Learning , vol.22 , pp. 59-94
    • Tsitsiklis, J.1    Van Roy, B.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.