Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/3688
Title: Playing is believing: the role of beliefs in multi-agent learning
Keywords: multi-agent learning algorithm
repeated games
belief
game theory
Matrix games
Nash equilibrium
Stochastic games
Reinforcement learning
PHC-Exploiter
Issue Date: 9-Oct-2013
Description: We propose a new classification for multi-agent learning algorithms, with each league of players characterized by both their possible strategies and possible beliefs. Using this classification, we review the optimality of existing algorithms and discuss some insights that can be gained. We propose an incremental improvement to the existing algorithms that seems to achieve average payoffs that are at least the Nash equilibrium payoffs in the long-run against fair opponents.
Singapore-MIT Alliance (SMA)
URI: http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers: http://hdl.handle.net/1721.1/3688
Appears in Collections:MIT Items

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