REINFORCEMENT LEARNING IN NEUROFUZZY TRAFFIC SIGNAL CONTROL

Ella Bingham
Helsinki University of Technology
Laboratory of Transportation Engineering

Contact address:
Helsinki University of Technology
Neural Networks Research Centre
P.O. Box 5400
FIN-02015 HUT 
Finland
tel. +358-9-451 5282 
fax +358-9-451 3277 
Email Ella.Bingham@hut.fi

European Journal of Operational Research, 2000 (in press)


ABSTRACT:

A fuzzy traffic signal controller uses simple ``if-then'' rules which involve 
linguistic concepts such as ``medium'' or ``long'',  presented as membership 
functions. In neurofuzzy traffic signal control, a neural network 
adjusts the fuzzy controller by fine-tuning the form and location of the 
membership functions. The learning algorithm of the neural network is
reinforcement learning, which gives credit for successful system behavior and
punishes for poor behavior; those actions that led to success
tend to be chosen more often in the future. The objective of the learning is 
to minimize the vehicular delay caused by the signal control policy. 
In simulation experiments, the learning algorithm is found successful 
at constant traffic volumes: the new membership functions produce smaller 
vehicular delay than the initial membership functions.

KEYWORDS: 

Fuzzy sets, neural networks, traffic signal control, reinforcement learning.