메뉴 건너뛰기




Volumn , Issue , 2010, Pages 191-198

Convergence, targeted optimality, and safety in multiagent learning

Author keywords

[No Author keywords available]

Indexed keywords

EMPIRICAL RESULTS; EXPLORATION AND EXPLOITATION; MODEL LEARNING; MULTI-AGENT LEARNING; MULTIAGENT LEARNING ALGORITHM; OPTIMALITY; REPEATED GAMES;

EID: 77956517473     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (18)

References (9)
  • 1
    • 9444299000 scopus 로고    scopus 로고
    • Performance bounded reinforcement learning in strategic interactions
    • Banerjee, Bikramjit and Peng, Jing. Performance bounded reinforcement learning in strategic interactions. In AAAI, pp. 2-7, 2004.
    • (2004) AAAI , pp. 2-7
    • Banerjee, B.1    Peng, J.2
  • 2
    • 36348967415 scopus 로고    scopus 로고
    • Convergence of gradient dynamics with a variable learning rate
    • Bowling, Michael and Veloso, Manuela. Convergence of gradient dynamics with a variable learning rate. In ICML, pp. 27-34, 2001.
    • (2001) ICML , pp. 27-34
    • Bowling, M.1    Veloso, M.2
  • 3
    • 0041965975 scopus 로고    scopus 로고
    • R-max- A general polynomial time algorithm for near-optimal reinforcement learning
    • Brafman, Ronen I. and Tennenholtz, Moshe. R-max - a general polynomial time algorithm for near-optimal reinforcement learning. J. Mach. Learn. Res., pp. 213-231, 2003.
    • (2003) J. Mach. Learn. Res. , pp. 213-231
    • Brafman, R.I.1    Tennenholtz, M.2
  • 5
    • 56049086673 scopus 로고    scopus 로고
    • Online multiagent learning against memory bounded adversaries
    • Chakraborty, Doran and Stone, Peter. Online multiagent learning against memory bounded adversaries. In ECML, pp. 211-226, 2008.
    • (2008) ECML , pp. 211-226
    • Chakraborty, D.1    Stone, P.2
  • 6
    • 34147159616 scopus 로고    scopus 로고
    • Awesome: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents
    • Conitzer, Vincent and Sandholm, Tuomas. Awesome: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents. In J. Mach. Learn. Res., pp. 23-43, 2006.
    • (2006) J. Mach. Learn. Res. , pp. 23-43
    • Conitzer, V.1    Sandholm, T.2
  • 7
    • 33745609272 scopus 로고    scopus 로고
    • Learning against opponents with bounded memory
    • Powers, Rob and Shoham, Yoav. Learning against opponents with bounded memory. In IJCAI, pp. 817-822, 2005.
    • (2005) IJCAI , pp. 817-822
    • Powers, R.1    Shoham, Y.2
  • 8
    • 34147097403 scopus 로고    scopus 로고
    • A general criterion and an algorithmic framework for learning in multi-agent systems
    • Powers, Rob, Shoham, Yoav, and Vu, Thuc. A general criterion and an algorithmic framework for learning in multi-agent systems. Mach. Learn., pp. 45-76, 2007.
    • (2007) Mach. Learn. , pp. 45-76
    • Powers, R.1    Shoham, Y.2    Vu, T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.