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Author:

Tao, Jun-Yuan (Tao, Jun-Yuan.) | Li, De-Sheng (Li, De-Sheng.) (Scholars:李德胜)

Indexed by:

CPCI-S

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.

Keyword:

multi-agent reinforcement learning cooperative behavior continuous state space

Author Community:

  • [ 1 ] [Tao, Jun-Yuan]Harbin Inst Technol, Dept Automat Measurement & Control, Harbin 150006, Peoples R China
  • [ 2 ] [Li, De-Sheng]Beijing Univ Technol, Dept Mech & Elect Engn, Beijing, Peoples R China

Reprint Author's Address:

  • [Tao, Jun-Yuan]Harbin Inst Technol, Dept Automat Measurement & Control, Harbin 150006, Peoples R China

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Source :

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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