Knowledge modeling by ELM in RL for SRHT problem

Abstract

Single Robot Hose Transport (SRHT) is a limit case of Linked Multicomponent Robotic Systems (L-MCRS), when one robot moves the tip of a hose to a desired position, while the other hose extreme is attached to a source position. Reinforcement Learning (RL) algorithms have been applied to learn autonomously the robot control with success. However, RL algorithms produce large and intractable data structures. This paper addresses the problem by learning an Extreme Learning Machine (ELM) from the state-action value Q-table, obtaining very relevant data reduction. In this paper we evaluate empirically a classification strategy to formulate ELM learning to provide approximations to the Q-table, obtaining very promising results.