Abstract
We present a new multiagent learning algorithm, RV σ(t), that builds on an earlier version, ReDVaLeR . ReDVaLeR could guarantee (a) convergence to best response against stationary opponents and either (b) constant bounded regret against arbitrary opponents, or (c) convergence to Nash equilibrium policies in self-play. But it makes two strong assumptions: (1) that it can distinguish between 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 learning rate of RV σ(t) that is explicitly dependent on time can overcome both of these assumptions. Consequently, RV σ(t) theoretically achieves (a’) convergence to near-best response against eventually stationary opponents, (b’) no-regret payoff 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 knowledge) at convergence of a no-regret algorithm in the Shapley game.
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Banerjee, B., Peng, J. (2006). Unifying Convergence and No-Regret in Multiagent Learning. In: Tuyls, K., Hoen, P.J., Verbeeck, K., Sen, S. (eds) Learning and Adaption in Multi-Agent Systems. LAMAS 2005. Lecture Notes in Computer Science(), vol 3898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11691839_5
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DOI: https://doi.org/10.1007/11691839_5
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