Reinforcement Learning Using Kohonen Feature Map Associative Memory with Refractoriness Based on Area Representation

摘要

In this paper, we propose a reinforcement learning method using Kohonen Feature Map Associative Memory with Refractoriness based on Area Representation. The proposed method is based on the actor-critic method, and the actor is realized by the Kohonen Feature Map Associative Memory with Refractoriness based on Area Representation. The Kohonen Feature Map Associative Memory with Refractoriness based on Area Representation is based on the self-organizing feature map, and it can realize successive learning and one-to-many associations. Moreover, it has robustness for noisy input and damaged neurons because it is based on the area representation. The proposed method makes use of this property in order to realize the learning during the practice of task. We carried out a series of computer experiments, and confirmed the effectiveness of the proposed method in path-finding problem.