Authors
Abstract
Learning Classifier Systems (LCS), are a machine learning technique which
combines reinforcement learning, evolutionary computing and other heuristics to
produce adaptive systems. The system HRC (Human – Rat - Cheese) focuses in
creating artificial creature (Rat) using computer simulation, and learning it how to
choose between two different basic behaviors, (approach / escape) combining them to
perform complex behavior, which represents the final response in changing
environment.
The HRC is built of two-classifier subsystems working together, each
classifier system learns a simple behavior, and the system as a whole has as its learning
goal the control of activities. Flat architecture was used. The flat organization allows
distinguishing between two different learning activities: the learning of basic behavior
and the learning of switch behavior. One classifier system learns basic behavior,
(approach/escape), i.e., it is used to learn the simulated robot single step movement in
every direction in the environment. Whereas the other classifier system learns to control
the activities of basic classifier systems, i.e., it is used to learn to choose between basic
behaviors using suppression as a composition mechanism to chose between two basic
behaviors which represent complex behavior.
Simple experiments were executed for HRC: comparing and contrasting the
effect of the reinforcement learning using reward & punishment with learning using
reward only. Experiment results show that the run using reinforcement learning with
reward only is unable to perform as well as the run with reinforcement learning with
reward and punishment.
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