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- W2116875315 abstract "In this paper, we first suggest a new type of Markov model extended by Watkins' action replay process [1]. The new Markov model is called multi-action replay process (MARP), which is a process designed for multiagent coordination on the basis of reward values, state transition probabilities, and equilibrium strategy taking account of joint-action among agents. Using this model, multiagent Q-learning algorithm is then constructed as a cooperative reinforcement learning algorithm under completely connected agents. Finally, we prove that multiagent Q-learning values converge to optimal values. Simulation results are reported to illustrate the validity of the proposed multiagent Q-learning algorithm." @default.
- W2116875315 created "2016-06-24" @default.
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- W2116875315 date "2010-09-01" @default.
- W2116875315 modified "2023-09-30" @default.
- W2116875315 title "Convergence of multiagent Q-learning: Multi action replay process approach" @default.
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- W2116875315 doi "https://doi.org/10.1109/isic.2010.5612911" @default.
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