Reinforcement learning-based mobile robot navigation
Nihal Altuntaş, Erkan İMAL, Nahit Emanet, Ceyda Nur Öztürk · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · 2016
In recent decades, reinforcement learning (RL) has been widely used in different research fields ranging from psychology to computer science. The unfeasibility of sampling all possibilities for continuous-state problems and the absence of an explicit teacher make RL algorithms preferable for supervised learning in the machine learning area, as the optimal control problem has become a popular subject of research. In this study, a system is proposed to solve mobile robot navigation by opting for the most popular two RL algorithms, Sarsa($\lambda )$ and Q($\lambda )$. The proposed system, developed in MATLAB, uses state and action sets, defined in a novel way, to increase performance. The system can guide the mobile robot to a desired goal by avoiding obstacles with a high success rate in both simulated and real environments. Additionally, it is possible to observe the effects of the initial parameters used by the RL methods, e.g., $\lambda $, on learning, and also to make comparisons between the performances of Sarsa($\lambda )$ and Q($\lambda )$ algorithms.