Trabalho de Conclusão de Curso de Graduação
Aprendizado por reforço profundo para navegação de robôs móveis
Fecha
2019-12-11Registro en:
JESUS, J. C. de. Aprendizado por reforço profundo para navegação de robôs móveis. 2019. 79 p. Trabalho de Conclusão de Curso (Graduação em Engenharia de Controle e Automação)- Universidade Federal de Santa Maria, Santa Maria, RS, 2019.
Autor
Jesus, Junior Costa de
Institución
Resumen
This work presents a study of deep reinforcement learning techniques that uses the Deep Deterministic
Policy Gradient network and the Soft Actor-Critic network for application in navigation
of mobile robots. In order for the robot to arrive to a target on a map, the networks have as input:
10 laser range findings, the previous linear and angular velocity, and relative position and
angle of the mobile robot to the target. As output, the network has the linear and angular velocity.
From the results analysis, it is possible to conclude that the deep reinforcement learning
algorithms, with continuous actions, are effective for the decision-making of robotic vehicles
and the Soft Actor-Critic networks present superior results, in less episodes, than the Deep Deterministic
Policy Gradient. However, it is necessary to create a good reward function for the
intelligent agent to accomplish its objectives. In order to show the performance of the Deep
Reinforcement Learning Algorithms, they were applied in experiments with a simulated robot
in three different environments and in a real robot in two environments.