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Volumn , Issue , 2004, Pages 598-605

Learning to fly by combining reinforcement learning with behavioural cloning

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACTING; AIRCRAFT; ALGORITHMS; BEHAVIORAL RESEARCH; MATHEMATICAL MODELS; PROBLEM SOLVING; SIMULATORS;

EID: 14344250066     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (26)

References (12)
  • 1
    • 14344264575 scopus 로고    scopus 로고
    • Behavioural cloning: Phenomena, results and problems. automated systems based on human skill
    • Berlin
    • Bratko, I., Urbančič, T., & Sammut, C. (1998). Behavioural cloning: phenomena, results and problems. automated systems based on human skill. IFAC Symposium. Berlin.
    • (1998) IFAC Symposium
    • Bratko, I.1    Urbančič, T.2    Sammut, C.3
  • 5
    • 14344251618 scopus 로고    scopus 로고
    • On learning how to play
    • van den H. Herik and J. Uiterwijk (Eds.). The Netherlands: Universiteit Maastricht
    • Morales, E. (1997). On learning how to play. In van den H. Herik and J. Uiterwijk (Eds.), Advances in computer chess 8, 235-250. The Netherlands: Universiteit Maastricht.
    • (1997) Advances in Computer Chess , vol.8 , pp. 235-250
    • Morales, E.1
  • 8
    • 0006385724 scopus 로고    scopus 로고
    • Rl-tops: An architecture for modularity and re-use in reinforcement learning
    • San Francisco: Morgan Kaufmann
    • Ryan, M. (1998). Rl-tops: An architecture for modularity and re-use in reinforcement learning. Proc. of the Fifteenth International Conference on Machine Learning (pp. 481-487). San Francisco: Morgan Kaufmann.
    • (1998) Proc. of the Fifteenth International Conference on Machine Learning , pp. 481-487
    • Ryan, M.1
  • 9
    • 14344260336 scopus 로고    scopus 로고
    • Using abstract models of behaviours to automatically generate reinforcement learning hierarchies
    • San Francisco: Morgan Kaufmann
    • Ryan, M. (2002). Using abstract models of behaviours to automatically generate reinforcement learning hierarchies. Proc. of the Nineteenth International Conference on Machine Learning (pp. 522-529). San Francisco: Morgan Kaufmann.
    • (2002) Proc. of the Nineteenth International Conference on Machine Learning , pp. 522-529
    • Ryan, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.