A Teaching Method by Using Self-Organizing Map for Reinforcement Learning
Takeshi Tateyama, Seiichi Kawata, Toshiki Oguchi · TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C · 2004
A new pre-teaching method for reinforcement learning using Self-Organizing Map (SOM) is described. The purpose of our study is to increase the learning rate using small number of teaching data generated by a human expert. In our method, the SOM is used to generate initial teaching data for the reinforcement learning agent from a few teaching data. The reinforcement learning function of the agent is initialized by using the teaching data generated by the SOM so as to increase the probability of selecting the optimal actions estimated by the SOM. Because the agent can get high rewards from the start of reinforcement learning, it is expected to increase the learning rate. The results of two computer simulations, mobile robot navigation and pursuit game, showed that the learning rate increased although the human expert had showed only a few teaching data.