Exploration of genetic network programming with two-stage reinforcement learning for mobile robot

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.


genetic network programming; inexperienced changes; two-stage reinforcement learning;

Full Text:


DOI: http://dx.doi.org/10.12928/telkomnika.v17i3.12232

Article Metrics

Abstract view : 267 times
PDF - 151 times


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.