The Effect of Sensory Information on Reinforcement Learning by a Robot Arm

Abstract

In this paper we present an application of ALECSYS, a distributed learning classifier system, to the control of a robot arm. ALECSYS is initialised with a set of randomly generated rules and is trained to control a robot arm whose task is to reach a non moving light source. At this point of our research our results are relative to the simulation of a real robot arm (IBM 7547 with a SCARA geometry), which will be the target of the final implementation of our learning system. INTRODUCTION ALECSYS (Dorigo, 1993; 1994), an implementation of a learning classifier system (Booker, Goldberg and Holland, 1989; Holland and Reitman, 1978) on a net of transputers, was utilised to train a robot arm to solve a light approaching task. This task, as well as more complicated ones, has already been learnt by ALECSYS implemented on AutonoMouse, a small autonomous robot (Colombetti and Dorigo, 1992; Dorigo and Colombetti, 1994). The main difference between the present and previous applications are, one, ...