Siti Sendari, Arif Nur Afandi, Ilham Ari Elbaith Zaeni, Yogi Dwi Mahandi, Kotaro Hirasawa, Hsien-I. Lin
This paper observes the exploration of Genetic Network Programming Two-Stage Reinforcement Learning for mobile robot navigation. The proposed method aims to observe its exploration when inexperienced environments used in the implementation. In order to deal with this situation, individuals are trained firstly in the training phase, that is, they learn the environment with ε-greedy policy and learning rate α parameters. Here, two cases are studied, i.e., case A for low exploration and case B for high exploration. In the implementation, the individuals implemented to get experience and learn a new environment on-line. Then, the performance of learning processes are observed due to the environmental changes. © 2019 Universitas Ahmad Dahlan.
Department of Electrical Engineering, Universitas Negeri Malang, Jalan Semarang No. 5 Malang, Jawa Timur, 65145, Indonesia; Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan; Graduate Institute of Automation, National Taipei Univeristy of Technology, Integrated Technology Complex, I, Sec. 3, Chung-Hsiao E Road, Taipei, 106, Taiwan
