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- W2092226735 abstract "In this paper, a game-theoretic framework based on the iterated prisoner's dilemma (IPD) is proposed to model the repeated dynamic interactions of multiple source nodes when communicating with multiple destinations in ad-hoc wireless networks. In such networks where nodes are autonomous, selfish, and not familiar with other nodes' strategies, fully cooperative behaviors cannot be assumed. Thus, a Q-learning algorithm is proposed to allow network nodes to adapt to and play the IPD game against opponents with a variety of known and unknown strategies. Simulation results illustrate that the proposed Q-learning algorithm allows network nodes to play optimally and achieve their maximum expected return values." @default.
- W2092226735 created "2016-06-24" @default.
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- W2092226735 date "2013-09-01" @default.
- W2092226735 modified "2023-09-25" @default.
- W2092226735 title "A game-theoretic framework with reinforcement learning for multinode cooperation in wireless networks" @default.
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- W2092226735 doi "https://doi.org/10.1109/pimrc.2013.6666280" @default.
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