A Simulation Study on Reinforcement Learning for Navigation Application

摘要

In this paper we have contributed work on implementation of Q-learning, a reinforcement, nonparameter based learning and decision making method.have formulated and demonstrated the Q-learning algorithm via simulation.The work includes formulation of a pseudo-code and development of algorithm taking into account of an application.The application of reinforcement learning is simulated through a conceptual agriculture field, where a robot is commanded to reach at trees and finally delivers the fruits to the storage point (goal).We have studied the effectiveness of g and a.The results show that the learning parameter (g) and the learning rate (a) are the two important parameters to be considered while developing Q-learning based reinforcement algorithm for specific application.We have also established the optimal values of g and a of an application through a simulation study.