Affiliations: [a]
Federal University of Rio Grande, Rio Grande, Rio Grande do Sul, Brazil
| [b]
Federal University of Santa Maria, Santa Maria, Rio Grande do Sul, Brazil
| [c]
Federal Institute of Espirito Santo, Serra, Espirito Santo, Brazil
| [d] Processing Department of Electricity, Federal University of Santa Maria, Santa Maria, RioGrande do Sul, Brazil
Correspondence:
[*]
Corresponding author. Daniel Fernando Tello Gamarra, Processing Department of Electricity, Federal University of Santa Maria, Santa Maria, Rio Grande do Sul, Brazil. E-mail: [email protected].
Abstract: This article describes the use of the Deep Deterministic Policy Gradient network, a deep reinforcement learning algorithm, for mobile robot navigation. The neural network structure has as inputs laser range findings, angular and linear velocities of the robot, and position and orientation of the mobile robot with respect to a goal position. The outputs of the network will be the angular and linear velocities used as control signals for the robot. The experiments demonstrated that deep reinforcement learning’s techniques that uses continuous actions, are efficient for decision-making in a mobile robot. Nevertheless, the design of the reward functions constitutes an important issue in the performance of deep reinforcement learning algorithms. In order to show the performance of the Deep Reinforcement Learning algorithm, we have applied successfully the proposed architecture in simulated environments and in experiments with a real robot.
Keywords: Deep Deterministic Policy Gradient, Deep Reinforcement Learning, Navigation for Mobile Robots
