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Volumn , Issue , 2005, Pages 91-98

On local rewards and scaling distributed reinforcement learning

Author keywords

[No Author keywords available]

Indexed keywords

DISTRIBUTED REINFORCEMENT LEARNING; GLOBAL REWARD SIGNALS; LOWER BOUNDS; MARKOV DECISION PROCESSES; MULTI-AGENT REINFORCEMENT LEARNING; NUMBER OF SAMPLES;

EID: 84864058248     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (26)

References (11)
  • 3
    • 84899032145 scopus 로고    scopus 로고
    • All learning is local: Multi-agent learning in global reward games
    • Y. Chang, T. Ho, and L. Kaelbling. All learning is local: Multi-agent learning in global reward games. In Advances in NIPS 14, 2004.
    • (2004) Advances in NIPS , vol.14
    • Chang, Y.1    Ho, T.2    Kaelbling, L.3
  • 4
    • 84899028010 scopus 로고    scopus 로고
    • Multi-agent planning with factored MDPs
    • C. Guestrin, D. Koller, and R. Parr. Multi-agent planning with factored MDPs. In NIPS-14, 2002.
    • (2002) NIPS-14
    • Guestrin, C.1    Koller, D.2    Parr, R.3
  • 6
    • 84880677563 scopus 로고    scopus 로고
    • Efficient reinforcement learning in factored mdps
    • M. Kearns and D. Koller. Efficient reinforcement learning in factored mdps. In IJCAI 16, 1999.
    • (1999) IJCAI , vol.16
    • Kearns, M.1    Koller, D.2
  • 7
    • 0141591857 scopus 로고    scopus 로고
    • Graphical models for game theory
    • M. Kearns, M. Littman, and S. Singh. Graphical models for game theory. In UAI, 2001.
    • (2001) UAI
    • Kearns, M.1    Littman, M.2    Singh, S.3
  • 11
    • 0012252296 scopus 로고
    • Tight performance bounds on greedy policies based on imperfect value functions
    • R. Williams and L. Baird. Tight performance bounds on greedy policies based on imperfect value functions. Technical report, Northeastern University, 1993.
    • (1993) Technical Report Northeastern University
    • Williams, R.1    Baird, L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.