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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Li Jun,PanQiShu.A multiagent reinforcement learning approach based on different states[J].Journal of Harbin Institute Of Technology(New Series),2010,17(3):419-423.DOI:10.11916/j.issn.1005-9113.2010.03.024.
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A multiagent reinforcement learning approach based on different states
Author NameAffiliation
Li Jun School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China 
PanQiShu School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China 
Abstract:
In this paper we describe a new reinforcement learning approach based on different states. When the multiagent is in coordination state,we take all coordinative agents as players and choose the learning approach based on game theory. When the multiagent is in indedependent state,we make each agent use the independent learning. We demonstrate that the proposed method on the pursuit-evasion problem can solve the dimension problems induced by both the state and the action space scale exponentially with the number of agents and no convergence problems,and we compare it with other related multiagent learning methods. Simulation experiment results show the feasibility of the algorithm.
Key words:  MAS  reinforcement learning  Q-learning  pursuit-evasion problem
DOI：10.11916/j.issn.1005-9113.2010.03.024
Clc Number:TP18
Fund:

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