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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks
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| 英文论文题目: |
FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks
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| 论文题目英文: |
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| 作者: |
Zhang, Zhen
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| 论文出处: |
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| 刊物名称: |
IEEE TRANSACTIONS ON CYBERNETICS
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| 年: |
2017
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| 卷: |
47 |
| 期: |
6 |
| 页: |
1367-1379 |
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| 收录类别: |
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| 影响因子: |
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| 摘要: |
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 agents' actions and only shares its state and reward at each step. We validate FMRQ through case studies of repeated games: four cases of two-player two-action and one case of three-player two-action. It is demonstrated that FMRQ can converge to one of the optimal Nash equilibria in these cases. Moreover, comparison experiments on tasks with multiple states and finite steps are conducted. One is box-pushing and the other one is distributed sensor network problem. Experimental results show that the proposed algorithm outperforms others with higher performance. |
| 英文摘要: |
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