Reinforcement Learning in Robotics

Abstract

Reinforcement learning has been found to offer to robotics the valid tools and techniques for the redesign of valuable and sophisticated designs for robotics. There are multiple challenges related to the prime problems related to the value added in the reinforcement of the new learning. The study has found the linkages between different subjects related to science in particular. We have attempted to make and establish the links that have been found between the two research communities in order to provide a survey-related task in reinforcement learning for behavior in terms of the generation that are found in the study. Many issues have been highlighted in the robot learning process that is used in their learning as well as various key programming tools and methods. We discuss how contributions that aimed towards taming the complexity of the domain of the study and determining representations and goals of RL. There has been a particular focus that is based on the goals of reinforcement learning that can provide the value-added function approaches and challenges in robotic reinforcement learning. The analysis has been conducted and has strived to demonstrate the value of reinforcement learning that has to be applied to different circumstances.