Multiagent reinforcement learning applied to a chase problem in a continuous world |
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Authors: | Hiroki Tamakoshi Shin Ishii |
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Affiliation: | (1) Nara Institute of Science and Technology, Takayama 8916-5, Ikoma, Nara, Japan;(2) Japan Science and Technology Corporation, CREST, Japan |
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Abstract: | Reinforcement learning (RL) is one of the methods of solving problems defined in multiagent systems. In the real world, the
state is continuous, and agents take continuous actions. Since conventional RL schemes are often defined to deal with discrete
worlds, there are difficulties such as the representation of an RL evaluation function. In this article, we intend to extend
an RL algorithm so that it is applicable to continuous world problems. This extension is done by a combination of an RL algorithm
and a function approximator. We employ Q-learning as the RL algorithm, and a neural network model called the normalized Gaussian
network as the function approximator. The extended RL method is applied to a chase problem in a continuous world. The experimental
result shows that our RL scheme was successful.
This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January
26–28, 2000 |
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Keywords: | Multiagent Reinforcement learning Continuous system Function approximation Normalized Gaussian network |
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