| Multi-agent reinforcement learning using modular neural network Q-learning algorithms |
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| 作者姓名： | 杨银贤 |
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| 作者单位： | Hefei Intelligent |
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| 摘 要： | 1 Introduction Reinforcement learning is a machine learningmethod for agents to acquire the optimal policyautonomously from the environment of their behaviors.When an action is executed, the agent receives areinforcement signal by interacting with theenvironment. This technology has recently been used inmany fields, such as robot control 1], artificialintelligence 2], especially multi-agent system 3,4].Generally, when the state space of the environment issmall enough and all states can be e…
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| 关 键 词： | 人工智能系统 计算方法 神经网络系统 机械学习 |
Multi-agent reinforcement learning using modular neural network Q-learning algorithms |
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| YANG Yin-xian,FANG Kai.Multi-agent reinforcement learning using modular neural network Q-learning algorithms[J].Journal of Chongqing University,2005,4(1):50-54. |
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| Authors: | YANG Yin-xian FANG Kai |
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| Abstract: | Reinforcement learning is an excellent approach which is used in artificial intelligence,automatic control, etc. However, ordinary reinforcement learning algorithm, such as Q-learning with lookup table cannot cope with extremely complex and dynamic environment due to the huge state space. To reduce the state space, modular neural network Q-learning algorithm is proposed, which combines Q-learning algorithm with neural network and module method. Forward feedback neural network, Elman neural network and radius-basis neural network are separately employed to construct such algorithm. It is revealed that Elman neural network Q-learning algorithm has the best performance under the condition that the same neural network training method, i.e. gradient descent error back-propagation algorithm is applied. |
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| Keywords: | reinforcement learning Q-learning neural network artificial intelligence |
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