STATE-ACTION VALUE FUNCTION MODELED BY ELM IN REINFORCEMENT LEARNING FOR HOSE CONTROL PROBLEMS

This paper addresses the problem of efficiency in reinforcement learning of Single Robot Hose Transport (SRHT) by training an Extreme Learning Machine (ELM) from the state-action value Q-table, obtaining large reduction in data space requirements because the number of ELM parameters is much less than the Q-table's size. Moreover, ELM implements a continuous map which can produce compact representations of the Q-table, and generalizations to increased space resolution and unknown situations. In this paper we evaluate empirically three strategies to formulate ELM learning to provide approximations to the Q-table, namely as classification, multi-variate regression and several independent regression problems.