Associative dynamics in a chaotic neural network
Neural Networks
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A method for analyzing the spatiotemporal changes of chaotic neural networks
Artificial Life and Robotics
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Humans learn from incidents in their own life and reflects these in subsequent actions as their own experiences. These experiences are memorized in the brain and recollected when necessary. This research incorporates this type of intelligent information processing mechanism and applies it to an autonomous agent. In the proposed system, the reinforcement Q-learning method is used. Autoassociative chaotic neural networks are also used as mutual associative memory systems. However, an agent cannot retrieve all stored patterns exactly, especially in the case of too many stored patterns and a strong correlation among them. To solve this problem, we propose to use types of attentive parameters and attentive characteristic patterns. The attentive characteristic pattern is part of the stored patterns. When robots concentrate their attention on a specific part of a stored pattern, i.e., the attentive characteristic pattern, whole stored patterns are retrieved easily and completely. Finally, the effectiveness of the proposed method is verified through a simulation applied to plural maze-searching problems.