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Volumn 4, Issue , 2005, Pages 3191-3196

Neural reinforcement learning to swing-up and balance a real pole

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL REINFORMCEMENT; Q-VALUE; SIMULATION MODEL; TRASITION MODEL;

EID: 27944453854     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (26)

References (10)
  • 1
    • 0001133021 scopus 로고
    • Generalization in reinforcement learning: Safely approximating the value function
    • Morgan Kaufmann
    • Boyan and Moore. Generalization in reinforcement learning: Safely approximating the value function. In Advances in Neural Information Processing Systems 7. Morgan Kaufmann, 1995.
    • (1995) Advances in Neural Information Processing Systems , vol.7
    • Boyan1    Moore2
  • 3
    • 84880694195 scopus 로고
    • Stable function approximation in dynamic programming
    • A. Prieditis and S. Russell, editors San Francisco, CA, Morgan Kaufmann
    • G. J. Gordon. Stable function approximation in dynamic programming. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 261-268, San Francisco, CA, 1995. Morgan Kaufmann.
    • (1995) Proceedings of the Twelfth International Conference on Machine Learning , pp. 261-268
    • Gordon, G.J.1
  • 5
    • 0000123778 scopus 로고
    • Self-improving reactive agents based on reinforcement learning, planning and teaching
    • L.-J. Lin. Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning, 8:293-321, 1992.
    • (1992) Machine Learning , vol.8 , pp. 293-321
    • Lin, L.-J.1
  • 7
    • 0033233953 scopus 로고    scopus 로고
    • Concepts and facilities of a neural reinforcement learning control architecture for technical process control
    • M. Riedmiller. Concepts and facilities of a neural reinforcement learning control architecture for technical process control. Journal of Neural Computing and Application, 8:323-338, 2000.
    • (2000) Journal of Neural Computing and Application , vol.8 , pp. 323-338
    • Riedmiller, M.1
  • 8
    • 84943274699 scopus 로고
    • A direct adaptive method for faster backpropagation learning: The RPROP algorithm
    • H. Ruspini, editor San Francisco
    • M. Riedmiller and H. Braun. A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In H. Ruspini, editor, Proceedings of the IEEE International Conference on Neural Networks (ICNN), pages 586 - 591, San Francisco, 1993.
    • (1993) Proceedings of the IEEE International Conference on Neural Networks (ICNN) , pp. 586-591
    • Riedmiller, M.1    Braun, H.2
  • 10
    • 0001046225 scopus 로고
    • Practical issues in temporal difference learning
    • G. Tesauro. Practical issues in temporal difference learning. Machine Learning, (8):257-277, 1992.
    • (1992) Machine Learning , Issue.8 , pp. 257-277
    • Tesauro, G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.