الفهرس 
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Abstract
One of the main problems in our modern society is traffic
congestion. This problem is mainly present in urban areas where road
infrastructure is often upgraded, but this modernization may almost
reach its limits, due to geographical, economic and environmental reasons. The continuous expansion of infrastructure is not feasible or even
desirable, which made the signal traffic controllers the most important
control mechanism for controlling the flow of traffic, especially at intersections. Researchers continue to try to introduce smart systems
to increase the efficiency of controlling units, such as replacing fixed
systems. These systems use different machine learning techniques to
enable signal control units to adjust and act based on the traffic situation. Here, reinforcement learning plays a role, as the learner, is also
called the agent, learns how to map the road condition to an action
that need to be taken to maximize a numerical reward. By taking
these actions and evaluating the reward, the agent must know which
actions lead to the best reward. These systems face two problems, the
need for coordination and the curse of dimensionality, which are two
main problems associated with the implementation of intelligent controllers using reinforcement learning, that’s because of the large number
of possible cases in the traffic network. An alternative approach is to
view the problem as a multi-agent system, in which a subset of the
controllers is controlled by a single agent. In this strategy, a number
of agents control the entire network and system and are no longer centralized, but rather distributed so that each agent acts on its own and
determines its behavior. Each agent only needs to learn the individual
behavior. One of the primary challenges is to ensure that the behavior
learned by the agent is optimal, to ensure this, some coordination is
needed between agents. So in addition to the challenge of dealing with
a very large space-action probability, the traffic field presents a major
challenge from the perspective of multiple agents since each agent actions depend on the actions of other surrounding agents. Under this
assumption, the coordination problem can be divided into several local
coordination problems and solved individually. The thesis addresses
these issues using a realistic simulation of the downtown of the city of
Ottawa which includes 65 intersection, it also shows that clustering the
network into smaller sub-networks solves the curse of dimensionality
iand moreover applying deep reinforcement learning implicitly covers
and solves the need for coordination between agents.