AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents

@article{Conitzer2006AWESOMEAG,
  title={AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents},
  author={Vincent Conitzer and T. Sandholm},
  journal={Machine Learning},
  year={2006},
  volume={67},
  pages={23-43}
}
  • Vincent Conitzer, T. Sandholm
  • Published 2006
  • Computer Science, Mathematics
  • Machine Learning
  • Two minimal requirements for a satisfactory multiagent learning algorithm are that it 1. learns to play optimally against stationary opponents and 2. converges to a Nash equilibrium in self-play. The previous algorithm that has come closest, WoLF-IGA, has been proven to have these two properties in 2-player 2-action (repeated) games—assuming that the opponent’s mixed strategy is observable. Another algorithm, ReDVaLeR (which was introduced after the algorithm described in this paper), achieves… CONTINUE READING
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