Autonomous navigation is challenging for mobile robots, especially in an unknown environment. Commonly, the robot requires multiple sensors to map the environment, locate itself, and make a plan to reach the target. However, reinforcement learning methods offer an alternative to map-free navigation tasks by learning the optimal actions to take. In this article, deep reinforcement learning agents are implemented using variants of the deep Q networks method, the D3QN and rainbow algorithms, for both the obstacle avoidance and the goal-oriented navigation task. The agents are trained and evaluated in a simulated environment. Furthermore, an analysis of the changes in the behaviour and performance of the agents caused by modifications in the reward function is conducted.
@article{arxiv.2302.09120,
title = {Robot path planning using deep reinforcement learning},
author = {Miguel Quinones-Ramirez and Jorge Rios-Martinez and Victor Uc-Cetina},
journal= {arXiv preprint arXiv:2302.09120},
year = {2023}
}