Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Parak, Roman; Matousek, Radomil

Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Files

132-Article Text-280-2-10-20210623.pdf(8.46 MB)

Date

2021-06-21

Authors

Parak, Roman

Matousek, Radomil

Publisher

Institute of Automation and Computer Science, Brno University of Technology

Altmetrics

Abstract

Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physical robots. In this paper, we compare several reinforcement  learning (Q-Learning, SARSA) and deep reinforcement learning (Deep Q-Network, Deep Sarsa) methods for a task aimed at achieving a specific goal using robotics arm UR3. The main optimization problem of this experiment is to find the best solution for each RL/DRL scenario and minimize the Euclidean distance accuracy error and smooth the resulting path by the Bézier spline method. The simulation and real word applications are controlled by the Robot Operating System (ROS). The learning environment is implemented using the OpenAI Gym library which uses the RVIZ simulation tool and the Gazebo 3D modeling tool for dynamics and kinematics.

Keywords

Reinforcement Learning, Deep neural network, Motion planning, Bézier spline, Robotics, UR3

Citation

Mendel. 2021 vol. 27, č. 1, s. 1-6. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/132

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0