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Volumn 2006, Issue , 2006, Pages 720-727

Probabilistic policy reuse in a reinforcement learning agent

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; INFORMATION MANAGEMENT; INFORMATION USE; PROBABILITY; REINFORCEMENT LEARNING;

EID: 34247199512     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1160633.1160762     Document Type: Conference Paper
Times cited : (289)

References (19)
  • 1
    • 84880690842 scopus 로고    scopus 로고
    • Bounding the suboptimality of reusing subproblems
    • M. Bowling and M. Veloso. Bounding the suboptimality of reusing subproblems. In Proceedings of IJCAI-99, 1999.
    • (1999) Proceedings of IJCAI-99
    • Bowling, M.1    Veloso, M.2
  • 3
    • 0002278788 scopus 로고    scopus 로고
    • Hierarchical reinforcement learning with the MAXQ value function decomposition
    • T. G. Dietterich. Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research, 13:227-303, 2000.
    • (2000) Journal of Artificial Intelligence Research , vol.13 , pp. 227-303
    • Dietterich, T.G.1
  • 5
    • 34247193582 scopus 로고    scopus 로고
    • Exploration and policy reuse
    • Technical Report CMU-CS-05-172, School of Computer Science, Carnegie Mellon University
    • F. Fernández and M. Veloso. Exploration and policy reuse. Technical Report CMU-CS-05-172, School of Computer Science, Carnegie Mellon University, 2005.
    • (2005)
    • Fernández, F.1    Veloso, M.2
  • 7
    • 29344474034 scopus 로고    scopus 로고
    • R. Maclin, J. Shavlik, L. Torrey, T. Walker, and E. Wild. Giving advice about preferred actions to reinforcement learners via, knowledge-based kernel regression. In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005. To appear.
    • R. Maclin, J. Shavlik, L. Torrey, T. Walker, and E. Wild. Giving advice about preferred actions to reinforcement learners via, knowledge-based kernel regression. In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005. To appear.
  • 8
    • 3843062299 scopus 로고    scopus 로고
    • Transfer of experience between reinforcement learning environments with progressive difficulty
    • M. G. Madden and T. Howley. Transfer of experience between reinforcement learning environments with progressive difficulty. Artificial Intelligence Review, 21:375-398, 2004.
    • (2004) Artificial Intelligence Review , vol.21 , pp. 375-398
    • Madden, M.G.1    Howley, T.2
  • 9
    • 27844586782 scopus 로고    scopus 로고
    • Concurrent Q-Learning: Reinforcement learning for dynamic goals and environments
    • R. B. Ollington and P. W. Vamplew. Concurrent Q-Learning: Reinforcement learning for dynamic goals and environments. International Journal of Intelligent Systems, 20:1037-1052, 2005.
    • (2005) International Journal of Intelligent Systems , vol.20 , pp. 1037-1052
    • Ollington, R.B.1    Vamplew, P.W.2
  • 15
    • 29444435242 scopus 로고    scopus 로고
    • M. E. Taylor, P. Stone, and Y. Liu. Value functions for RL-based behavior transfer: A comparative study. In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005. To appear.
    • M. E. Taylor, P. Stone, and Y. Liu. Value functions for RL-based behavior transfer: A comparative study. In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005. To appear.
  • 16
    • 0003411271 scopus 로고
    • Efficient exploration in reinforcement learning
    • I-CS-92-102, Carnegie Mellon University, January
    • S. Thrun. Efficient exploration in reinforcement learning. Technical Report C,I-CS-92-102, Carnegie Mellon University, January 1992.
    • (1992) Technical Report C
    • Thrun, S.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.