Towards continuous actions in continuous space and time using self-adaptive constructivism in neural XCSF

  • Authors:
  • Gerard David Howard;Larry Bull;Pier-Luca Lanzi

  • Affiliations:
  • University of the West of England, Bristol, United Kingdom;University of the West of England, Bristol, United Kingdom;Politecnico di Milano, Milan, Italy

  • Venue:
  • Proceedings of the 11th Annual conference on Genetic and evolutionary computation
  • Year:
  • 2009

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Abstract

This paper presents a Learning Classifier System (LCS) where each classifier condition is represented by a feed-forward multi-layered perceptron (MLP) network. Adaptive behavior is realized through the use of self-adaptive parameters and neural constructivism, providing the system with a flexible knowledge representation. The approach allows for the evolution of networks of appropriate complexity to solve a continuous maze environment, here using either discrete-valued actions, continuous-valued actions, or continuous-valued actions of continuous duration. In each case, it is shown that the neural LCS employed is capable of developing optimal solutions to the reinforcement learning task presented in this paper.