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Abstract:
Reinforcement learning is a powerful method for solving sequential decision making problems. But it is difficult to apply to practical problems such as multi-agent systems with continuous state space problems. In this paper we present a cooperative strategy learning method to solve the problem. It combines WOLF-PHC algorithms with function approximation of RL techniques. By this method an agent could learn cooperative behavior in the multi-agent environment with continuous state space. Using a subtask of RoboCup soccer, Keepaway, we demonstrate the effective of this learning method and the experiment results show that the algorithm converges.
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PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7
Year: 2006
Page: 2107-,
Language: English
Cited Count:
WoS CC Cited Count: 6
SCOPUS Cited Count:
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 1
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