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- W2169792277 abstract "In this paper, the multi-agent learning methods in an uncertain environment are addressed. The advantages and disadvantages of each algorithm are given. Rationality and convergence are the two main properties of multi-agent learning algorithms. However, it is very difficult to achieve both properties simultaneously. Minmax-Q learning is guaranteed to converge to equilibrium but there is no guarantee that this is the best response to the actual opponent. Therefore, Minmax-Q is not rational. In contrast, opponent modeling is rational but not convergent. Reinforcement learning using a variable learning rate and simultaneously achieves both properties. To reduce the dimension of state space, modular Q-learning and multilayered reinforcement learning are presented. The presented methods are not exhaustive, but they highlight the major methods used by researchers in the past years." @default.
- W2169792277 created "2016-06-24" @default.
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- W2169792277 date "2003-06-26" @default.
- W2169792277 modified "2023-10-14" @default.
- W2169792277 title "Multi-agent learning methods in an uncertain environment" @default.
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- W2169792277 doi "https://doi.org/10.1109/icmlc.2002.1174416" @default.
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