Comparison of Various Reinforcement Learning Environments in the Context of Continuum Robot Control

Abstract

Controlling flexible and continuously structured continuum robots is a challenging task in the field of robotics and control systems. This study explores the use of reinforcement learning (RL) algorithms in controlling a three-section planar continuum robot. The study aims to investigate the impact of various reward functions on the performance of the RL algorithm. The RL algorithm utilized in this study is the Deep Deterministic Policy Gradient (DDPG), which can be applied to both continuous-state and continuous-action problems. The study’s findings reveal that the design of the RL environment, including the selection of reward functions, significantly influences the performance of the RL algorithm. The study provides significant information on the design of RL environments for the control of continuum robots, which may be valuable to researchers and practitioners in the field of robotics and control systems.