Reinforcement Learning by KFM Probabilistic Associative Memory Based on Weights Distribution and Area Neuron Increase and Decrease

In this paper, we propose a reinforcement learning method using Kohonen Feature Map Probabilistic Associative Memory based on Weights Distribution and Area Neuron and Increase and Decrease (KFMPAM-WD-NID). The proposed method is based on the actorcritic method, and the actor is realized by the KFMPAM-WD-NID. The KFMPAM-WD-NID is based on the self-organizing feature map, and it can realize successive learning and one-to-many associations. Moreover, the weights distribution in the Map Layer can be modified by the increase and decrease of neurons in each area. The proposed method makes use of these properties 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 the pursuit problem.