@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Bikramjit Banerjee's publication pages at
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@InCollection{Banerjee06:Unifying,
  author = 	 {B. Banerjee and J. Peng},
  title = 	 {Unifying Convergence and No-regret in Multiagent Learning},
  booktitle = 	 {Learning and Adaption in Multi-Agent Systems},
  pages = 	 {100 - 114},
  publisher = {Springer-Verlag},
  year = 	 {2006},
  editor = 	 {Karl Tuyls and Pieter Jan 't Hoen and Sandip Sen and Katja Verbeeck},
  volume = 	 {LNAI 3898},
  abstract = {            We present a new multiagent learning algorithm, RVσ(t) , that builds
on an earlier version, ReDVaLeR . ReDVaLeR could guarantee (a) convergence
to best response against stationary opponents and either (b) constant bounded re-
gret against arbitrary opponents, or (c) convergence to Nash equilibrium policies
in self-play. But it makes two strong assumptions: (1) that it can distinguish be-
tween self-play and otherwise non-stationary agents and (2) that all agents know
their portions of the same equilibrium in self-play. We show that the adaptive
learnng rate of RVσ(t) that is explicitly dependent on time can overcome both of
these assumptions. Consequently, RVσ(t) theoretically achieves (a’) convergence
to near-best response against eventually stationary opponents, (b’) no-regret pay-
off against arbitrary opponents and (c’) convergence to some Nash equilibrium
policy in some classes of games, in self-play. Each agent now needs to know
its portion of any equilibrium, and does not need to distinguish among non-
stationary opponent types. This is also the first successful attempt (to our knowl-
edge) at convergence of a no-regret algorithm in the Shapley game.
}
}