Comparing learning classifier systems for continuous-valued online environments

Stone, C. and Bull, L. (2003) Comparing learning classifier systems for continuous-valued online environments. UWE Learning Classifier System Group: Technical Report (LSCG03). Available from:

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We investigate Learning Classifier Systems for online environments that consist of real-valued states and which require every action made by the agent to count towards its performance. Two Learning Classifier System architectures are considered, ZCS and XCS. We use an interval representation with these Learning Classifier Systems for the rule conditions together with roulette wheel action selection. As real-world environments are rarely deterministic, we investigate the performance of these two Learning Classifier System architectures on a set of artificial environments with stochastic reward functions. We briefly review related work and relate this to the experiments performed in this paper.

Item Type:Article
Uncontrolled Keywords:learning, classifier systems, continuous-valued, online environments
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:20754
Deposited By: M. Clarke
Deposited On:12 Jul 2013 07:48
Last Modified:15 Nov 2016 22:21

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