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Adaption of XCS to Multi-Learner Predator/Prey Scenarios


Clemens Lode, Urban RichterHartmut Schmeck



Published: 2010 Juli
Herausgeber: Martin Pelikan, Jürgen Branke
Buchtitel: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO 2010)
Seiten: 1015-1022
Verlag: ACM
Erscheinungsort: New York, NY, USA
Referierte Veröffentlichung
BibTeX

Kurzfassung
Learning classifier systems (LCSs) are rule-based evolutionary reinforcement learning systems. Today, especially variants of Wilson’s extended classifier system (XCS) are widely applied for machine learning. Despite their widespread application, LCSs have drawbacks, e. g., in multi-learner scenarios, since the Markov property is not fulfilled.

In this paper, LCSs are investigated in an instance of the generic homogeneous and non-communicating predator/prey scenario. A group of predators collaboratively observe a (randomly) moving prey as long as possible, where each predator is equipped with a single, independent XCS. Results show that improvements in learning are achieved by cleverly adapting a multi-step approach to the characteristics of the investigated scenario. Firstly, the environmental reward function is expanded to include sensory information. Secondly, the learners are equipped with a memory to store and analyze the history of local actions and given payoffs.

ISBN: 978-1-4503-0072-8
DOI Link: 10.1145/1830483.1830669

Projekt

OCCSOCCS (Phase III)



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet
Agenten Systeme, Maschinelles Lernen, Organic Computing