| dc.description | Since the establishment of robotics in industrial applications, industrial robot programming involves the repetitive and
time-consuming process of manually specifying a fixed trajectory, resulting in machine idle time in production and the
necessity of completely reprogramming the robot for different tasks. The increasing number of robotics applications
in unstructured environments requires not only intelligent but also reactive controllers due to the unpredictability
of the environment and safety measures, respectively. This paper presents a comparative analysis of two classes of
Reinforcement Learning algorithms, value iteration (Q-Learning/DQN) and policy iteration (REINFORCE), applied to
the discretized task of positioning a robotic manipulator in an obstacle-filled simulated environment, with no previous
knowledge of the obstacles??? positions or of the robot arm dynamics. The agent???s performance and algorithm convergence
are analyzed under different reward functions and on four increasingly complex test projects: 1-Degree of Freedom
(DOF) robot, 2-DOF robot, Kuka KR16 Industrial robot, Kuka KR16 Industrial robot with random setpoint/obstacle
placement. The DQN algorithm presented significantly better performance and reduced training time across all test
projects, and the third reward function generated better agents for both algorithms. | |
