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Volumn 2, Issue , 2013, Pages 1061-1068

Object focused Q-learning for autonomous agents

Author keywords

Modular RL; Reinforcement learning; State abstraction; Task decomposition

Indexed keywords

AUTONOMOUS AGENTS; LEARNING ALGORITHMS; MULTI AGENT SYSTEMS; RISK PERCEPTION;

EID: 84899422939     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (28)

References (22)
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  • 2
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    • The MAXQ method for hierarchical reinforcement learning
    • DIETTERICH, T. The MAXQ method for hierarchical reinforcement learning. In Proc. Int. Conf. on Machine Learning (1998), pp. 118-126.
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  • 3
    • 56449093331 scopus 로고    scopus 로고
    • An object-oriented representation for efficient reinforcement learning
    • ACM
    • DIUK, C, COHEN, A., and LITTMAN, M. An object-oriented representation for efficient reinforcement learning. In Proc. Int. Conf. on Machine Learning (2008), ACM, pp. 240-247.
    • (2008) Proc. Int. Conf. on Machine Learning , pp. 240-247
    • Diuk, C.1    Cohen, A.2    Littman, M.3
  • 6
    • 0030193409 scopus 로고    scopus 로고
    • PALO: A probabilistic hill-climbing algorithm
    • GREINER, R. PALO: A probabilistic hill-climbing algorithm. Artificial Intelligence 84, 1 (1996), 177-208.
    • (1996) Artificial Intelligence 84 , vol.1 , pp. 177-208
    • Greiner, R.1
  • 8
    • 0013465036 scopus 로고    scopus 로고
    • Discovering hierarchy in reinforcement learning with HEXQ
    • HENGST, B. Discovering hierarchy in reinforcement learning with HEXQ. In Proc. Int. Conf. on Machine Learning (2002), pp. 234-250.
    • (2002) Proc. Int. Conf. on Machine Learning , pp. 234-250
    • Hengst, B.1
  • 11
    • 26444479778 scopus 로고
    • Optimization by simulated annealing
    • KIRKPATRICK, S., and GELATT., C. D., and VECCHI, M. P. Optimization by simulated annealing. Science 220, 4598 (1983), 671-680.
    • (1983) Science , vol.220 , Issue.4598 , pp. 671-680
    • Kirkpatrick, S.1    Gelatt, C.D.2    Vecchi, M.P.3
  • 15
    • 14344261491 scopus 로고    scopus 로고
    • Using relative novelty to identify useful temporal abstractions in reinforcement learning
    • SIMSEK, O., and BARTO, A. G. Using relative novelty to identify useful temporal abstractions in reinforcement learning. Proc. Int. Conf. on Machine Learning (2004), 95.
    • (2004) Proc. Int. Conf. on Machine Learning , pp. 95
    • Simsek, O.1    Barto, A.G.2
  • 19
    • 37249061374 scopus 로고    scopus 로고
    • A survey of reinforcement learning in relational domains
    • ISSN 1381-3625
    • VAN OTTERLO, M. A survey of reinforcement learning in relational domains. In CTIT Technical Report Series, ISSN 1381-3625 (2005).
    • (2005) CTIT Technical Report Series
    • Van Otterlo, M.1
  • 20
    • 80054969173 scopus 로고    scopus 로고
    • Intrinsically motivated hierarchical skill learning in structured environments
    • VIGORITO, C. M., and BARTO, A. G. Intrinsically Motivated Hierarchical Skill Learning in Structured Environments. IEEE Trans, on Autonomous Mental Development 2, 2 (2010), 132-143.
    • (2010) IEEE Trans, on Autonomous Mental Development , vol.2 , Issue.2 , pp. 132-143
    • Vigorito, C.M.1    Barto, A.G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.