Comparison of multiple reinforcement learning and deep reinforcement learning methods for the task aimed at achieving the goal

Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physi-cal 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 goal using robotics arm UR3. The main optimization problem of this experiment is to find the best solution for each RL/DRL scenario, respectively, minimize the Euclidean distance accuracy error and smooth the resulting path by the Bézier spline method. The simulation and real word application 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. © 2021, Brno University of Technology. All rights reserved.