Detail publikace
Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal
PARÁK, R. MATOUŠEK, R.
Anglický název
Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal
Typ
článek v časopise ve Scopus, Jsc
Jazyk
en
Originální abstrakt
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.
Anglický abstrakt
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.
Klíčová slova anglicky
Reinforcement Learning, Deep neural network, Motion planning, Bézier spline, Robotics, UR3
Vydáno
21.06.2021
Nakladatel
Brno University of Technology
Místo
Brno University of Technology
ISSN
1803-3814
Ročník
27
Číslo
1
Strany od–do
1–8
Počet stran
8
BIBTEX
@article{BUT172507,
author="Roman {Parák} and Radomil {Matoušek},
title="Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal",
year="2021",
volume="27",
number="1",
month="June",
pages="1--8",
publisher="Brno University of Technology",
address="Brno University of Technology",
issn="1803-3814"
}