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Abstract:
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, including the case of
interleague play. 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.
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