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Volumn , Issue , 2009, Pages 793-800

Binary action search for learning continuous-action control policies

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POLICIES; ACTION SPACES; AUGMENTED STATE SPACE; CONTINUOUS STATE; CONTROL POLICY; DISCRETIZATIONS; DOUBLE INTEGRATOR; INVERTED PENDULUM; LEAST SQUARE; NOVEL METHODS; POLICY ITERATION; REAL-WORLD PROBLEM; REINFORCEMENT LEARNING METHOD; STOCHASTIC PROCESS;

EID: 71149094455     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (33)

References (18)
  • 1
    • 0003477315 scopus 로고
    • Reinforcement learning with high-dimensional, continuous actions
    • WL-TR-93-1147, Wright Laboratory
    • Baird, L. C., & Klopf, A. H. (1993). Reinforcement learning with high-dimensional, continuous actions (Technical Report WL-TR-93-1147). Wright Laboratory.
    • (1993) Technical Report
    • Baird, L.C.1    Klopf, A.H.2
  • 7
    • 85161968592 scopus 로고    scopus 로고
    • Reinforcement learning in continuous action spaces through sequential monte carlo methods
    • Lazaric, A., Restelli, M., & Bonarini, A. (2008). Reinforcement learning in continuous action spaces through sequential monte carlo methods. In Advances in neural information processing systems 20, 833-840.
    • (2008) Advances in neural information processing systems , vol.20 , pp. 833-840
    • Lazaric, A.1    Restelli, M.2    Bonarini, A.3
  • 12
    • 67650370700 scopus 로고    scopus 로고
    • Application of a self-learning controller with continuous control signals based on the DOE-approach
    • Riedmiller, M. (1997). Application of a self-learning controller with continuous control signals based on the DOE-approach. Proceedings of the European Symposium on Neural Networks (pp. 237-242).
    • (1997) Proceedings of the European Symposium on Neural Networks , pp. 237-242
    • Riedmiller, M.1
  • 13
    • 32844474095 scopus 로고    scopus 로고
    • Reinforcement learning with factored states and actions
    • Sallans, B., & Hinton, G. E. (2004). Reinforcement learning with factored states and actions. Journal of Machine Learning Research, 5, 1063-1088.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 1063-1088
    • Sallans, B.1    Hinton, G.E.2
  • 14
    • 0031231885 scopus 로고    scopus 로고
    • Experiments with reinforcement learning in problems with continuous state and action spaces
    • Santamaría, J. C., Sutton, R. S., & Ram, A. (1998). Experiments with reinforcement learning in problems with continuous state and action spaces. Adaptive Behavior, 6, 163-218.
    • (1998) Adaptive Behavior , vol.6 , pp. 163-218
    • Santamaría, J.C.1    Sutton, R.S.2    Ram, A.3
  • 17
    • 0031341345 scopus 로고    scopus 로고
    • Neural reinforcement learning for behaviour synthesis
    • Touzet, C. (1997). Neural reinforcement learning for behaviour synthesis. Robotics and Autonomous Systems, 22, 251-281.
    • (1997) Robotics and Autonomous Systems , vol.22 , pp. 251-281
    • Touzet, C.1
  • 18
    • 0030082891 scopus 로고    scopus 로고
    • An approach to fuzzy control of nonlinear systems: Stability and design issues
    • Wang, H., Tanaka, K., & Griffin, M. (1996). An approach to fuzzy control of nonlinear systems: Stability and design issues. IEEE Trans. on Fuzzy Systems, 4, 14-23.
    • (1996) IEEE Trans. on Fuzzy Systems , vol.4 , pp. 14-23
    • Wang, H.1    Tanaka, K.2    Griffin, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.