Evolutionary reinforcement learning for neurofuzzy control
Mohammad Sayem Chowdhury, Yizhe Li · 1997
. Disadvantages of traditional reinforcement learning techniques are complicated structures and that training algorithms are often reliant on the derivative information of the problem domain and also require a priori information of the network architecture. Such handicaps are overcome in this paper with the use of `messy genetic algorithms', whose main characteristic is a variable length chromosome. This paper represents a novel approach to globally optimised and on-line learning fuzzy controllers for cases where supervised learning is difficult. The design method is based on the functional reasoning of fuzzy logic combined with reinforcement learning paradigm. In addition to the structural optimisation of the neurofuzzy network, the messy genetic algorithm has shown to be extremely flexible and computationally efficient. In keeping with reinforcement learning tradition, the evolutionary reinforcement learning based neurofuzzy controller is applied to the potentially unstable non-linea...