Reinforcement Learning-Based Optimal Multiple Waypoint Navigation
Christos Vlachos, Panagiotis Rousseas, Charalampos P. Bechlioulis, Kostas J. Kyriakopoulos
Abstract
In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.
BibTeX
@inproceedings{icra2023_reinforcementlea,
title = {Reinforcement Learning-Based Optimal Multiple Waypoint Navigation},
author = {Christos Vlachos and Panagiotis Rousseas and Charalampos P. Bechlioulis and Kostas J. Kyriakopoulos},
booktitle = {ICRA 2023},
year = {2023}
}