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  2006, Vol. 19 Issue (3): 428-432    DOI:
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ForemostPolicy Reinforcement Learning Based ART2 Neural Network
FAN Jian1,2, WU GengFeng1
1.School of Computer Engineering and Science, Shanghai University, Shanghai 200072
2.Nanjing Army Command College, Nanjing 210045

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Abstract  A foremostpolicy reinforcement learning based ART2 neural network (FPRLART2) and its learning algorithm are proposed in this paper. To fit the requirement of real time learning, the first awarded behavior based on present states is selected in our ForemostPolicy Reinforcement Learning (FPRL) in stead of the optimal behavior in 1step QLearning. The algorithm of FPRL is given and it is integrated with ART2 neural network. The stored weights of classified pattern in ART2 is increased or decreased by reinforcement learning. The FPRLART2 is successfully used in collision avoidance of mobile robot and the simulation experiment indicates that the times of collision between robot and obstacle is effectively decreased. The FPRLART2 makes favorable result of collision avoidance.
Key wordsReinforcement Learning      ART2 Neural Network      ForemostPolicy      Collision Avoidance     
Received: 19 December 2004     
ZTFLH: TP18  
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FAN Jian
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FAN Jian,WU GengFeng. ForemostPolicy Reinforcement Learning Based ART2 Neural Network[J]. , 2006, 19(3): 428-432.
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