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Volumn 8, Issue 4, 1999, Pages 323-338

Concepts and facilities of a neural reinforcement learning control architecture for technical process control

Author keywords

Intelligent control; Neural control; Reinforcement learning; Technical process control

Indexed keywords


EID: 0033233953     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s005210050038     Document Type: Article
Times cited : (30)

References (16)
  • 3
    • 0003602259 scopus 로고
    • Technical Report COINS TR 89-95, Department of Computer and Information Science, University of Massachusetts, Amherst, September
    • Barto AG, Sutton RS, Watkins CJCH. Learning and sequential decision making. Technical Report COINS TR 89-95, Department of Computer and Information Science, University of Massachusetts, Amherst, September 1989
    • (1989) Learning and Sequential Decision Making
    • Barto, A.G.1    Sutton, R.S.2    Cjch, W.3
  • 5
    • 0003259931 scopus 로고    scopus 로고
    • Improving elevator performance using reinforcement learning
    • ME Hasselmo, DS Touretzky, MC Mozer, editors, MIT Press
    • Barto AG, Crites RH. Improving elevator performance using reinforcement learning. In: ME Hasselmo, DS Touretzky, MC Mozer, editors, Advances in Neural Information Processing Systems 8, MIT Press, 1996
    • (1996) Advances in Neural Information Processing Systems , vol.8
    • Barto, A.G.1    Crites, R.H.2
  • 10
    • 0003787146 scopus 로고
    • Princeton University Press, Princeton, NJ
    • Bellman RE. Dynamic Programming, Princeton University Press, Princeton, NJ, 1957
    • (1957) Dynamic Programming
    • Bellman, R.E.1
  • 13
    • 33847202724 scopus 로고
    • Learning to predict by the methods of temporal differences
    • Sutton RS. Learning to predict by the methods of temporal differences. Machine Learning 1988; (3): 9-44
    • (1988) Machine Learning , Issue.3 , pp. 9-44
    • Sutton, R.S.1
  • 15
    • 0009267623 scopus 로고    scopus 로고
    • Generating continuous control signals for reinforcement controllers using dynamic output elements
    • Bruges
    • Riedmiller M. Generating continuous control signals for reinforcement controllers using dynamic output elements. European Symposium on Artificial Neural Networks (ESANN '97), Bruges, 1997
    • (1997) European Symposium on Artificial Neural Networks (ESANN '97)
    • Riedmiller, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.