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Volumn , Issue , 2015, Pages 1-11

Language understanding for text-based games using deep reinforcement learning

Author keywords

[No Author keywords available]

Indexed keywords

INTERACTIVE COMPUTER GRAPHICS; MACHINE LEARNING; NATURAL LANGUAGE PROCESSING SYSTEMS; REINFORCEMENT LEARNING; SEMANTICS; VIRTUAL REALITY;

EID: 84959861546     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/d15-1001     Document Type: Conference Paper
Times cited : (337)

References (29)
  • 1
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    • High-level reinforcement learning in strategy games
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    • Christopher Amato and Guy Shani. 2010. High-level reinforcement learning in strategy games. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Volume 1, pages 75-82. International Foundation for Autonomous Agents and Multiagent Systems.
    • (2010) Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems , vol.1 , pp. 75-82
    • Amato, C.1    Shani, G.2
  • 2
    • 35048863614 scopus 로고    scopus 로고
    • Adventure games: A challenge for cognitive robotics
    • Eyal Amir and Patrick Doyle. 2002. Adventure games: A challenge for cognitive robotics. In Proc. Int. Cognitive Robotics Workshop, pages 148-155.
    • (2002) Proc. Int. Cognitive Robotics Workshop , pp. 148-155
    • Amir, E.1    Doyle, P.2
  • 11
    • 84883072695 scopus 로고    scopus 로고
    • Speaking with your sidekick: Understanding situated speech in computer role playing games
    • R. Michael Young and John E. Laird, editors, June 1-5, 2005, Marina del Rey, California, USA, AAAI Press
    • Peter Gorniak and Deb Roy. 2005. Speaking with your sidekick: Understanding situated speech in computer role playing games. In R. Michael Young and John E. Laird, editors, Proceedings of the First Artificial Intelligence and Interactive Digital Entertainment Conference, June 1-5, 2005, Marina del Rey, California, USA, pages 57-62. AAAI Press.
    • (2005) Proceedings of the First Artificial Intelligence and Interactive Digital Entertainment Conference , pp. 57-62
    • Gorniak, P.1    Roy, D.2
  • 15
    • 84906930657 scopus 로고    scopus 로고
    • Learning to automatically solve algebra word problems
    • Nate Kushman, Yoav Artzi, Luke Zettlemoyer, and Regina Barzilay. 2014. Learning to automatically solve algebra word problems. ACL (1), pages 271-281.
    • (2014) ACL , Issue.1 , pp. 271-281
    • Kushman, N.1    Artzi, Y.2    Zettlemoyer, L.3    Barzilay, R.4
  • 16
    • 84923510502 scopus 로고    scopus 로고
    • Learning to parse natural language commands to a robot control system
    • Springer
    • Cynthia Matuszek, Evan Herbst, Luke Zettlemoyer, and Dieter Fox. 2013. Learning to parse natural language commands to a robot control system. In Experimental Robotics, pages 403-415. Springer.
    • (2013) Experimental Robotics , pp. 403-415
    • Matuszek, C.1    Herbst, E.2    Zettlemoyer, L.3    Fox, D.4
  • 19
    • 0027684215 scopus 로고
    • Prioritized sweeping: Reinforcement learning with less data and less time
    • Andrew W Moore and Christopher G Atkeson. 1993. Prioritized sweeping: Reinforcement learning with less data and less time. Machine Learning, 13(1):103-130.
    • (1993) Machine Learning , vol.13 , Issue.1 , pp. 103-130
    • Moore, A.W.1    Atkeson, C.G.2
  • 21
    • 84880900542 scopus 로고    scopus 로고
    • Reinforcement learning of local shape in the game of go
    • David Silver, Richard S Sutton, and Martin Miiller. 2007. Reinforcement learning of local shape in the game of go. In IJCAI, volume 7, pages 1053-1058.
    • (2007) IJCAI , vol.7 , pp. 1053-1058
    • Silver, D.1    Sutton, R.S.2    Miiller, M.3
  • 24
    • 84867399396 scopus 로고    scopus 로고
    • Reinforcement learning in games
    • Springer
    • Istvan Szita. 2012. Reinforcement learning in games. In Reinforcement Learning, pages 539-577. Springer.
    • (2012) Reinforcement Learning , pp. 539-577
    • Szita, I.1
  • 26
    • 84893343292 scopus 로고    scopus 로고
    • Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
    • Tijmen Tieleman and Geoffrey Hinton. 2012. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4.
    • (2012) COURSERA: Neural Networks for Machine Learning , pp. 4
    • Tieleman, T.1    Hinton, G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.