Due to the rapid growth of urban vehicles, traffic congestion has become
more serious. The signalized intersections are used all over the world and
still established in the new construction. This paper proposes a self-adapted
approach, called evolutionary reinforcement learning multi-agents system
(ERL-MA), which combines computational intelligence and machine
learning. The concept of this work is to build an intelligent agent capable of
developing senior skills to manage the traffic light control system at any
type of junction, using two powerful tools: learning from the confronted
experience and the assumption using the randomization concept. The
ERL-MA is an independent multi-agents system composed of two layers:
the modeling and the decision layers. The modeling layer uses the
intersection modeling using generalized fuzzy graph technique. The decision
layer uses two methods: the novel greedy genetic algorithm (NGGA), and
the Q-learning. In the Q-learning method, a multi Q-tables strategy and a
new reward formula are proposed. The experiments used in this work relied
on a real case of study with a simulation of one-hour scenario at Pasubio
area in Italy. The obtained results show that the ERL-MA system succeeds
to achieve competitive results comparing to urban traffic optimization by
integrated automation (UTOPIA) system using different metrics.

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