FMRQ—A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks

@article{Zhang2017FMRQAMR,
  title={FMRQ—A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks},
  author={Zhen Zhang and Dongbin Zhao and Junwei Gao and Dongqing Wang and Yujie Dai},
  journal={IEEE Transactions on Cybernetics},
  year={2017},
  volume={47},
  pages={1367-1379}
}
In this paper, we propose a multiagent reinforcement learning algorithm dealing with fully cooperative tasks. The algorithm is called frequency of the maximum reward Q-learning (FMRQ). FMRQ aims to achieve one of the optimal Nash equilibria so as to optimize the performance index in multiagent systems. The frequency of obtaining the highest global immediate reward instead of immediate reward is used as the reinforcement signal. With FMRQ each agent does not need the observation of the other… CONTINUE READING

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