1,689 research outputs found
CoLight: Learning Network-level Cooperation for Traffic Signal Control
Cooperation among the traffic signals enables vehicles to move through
intersections more quickly. Conventional transportation approaches implement
cooperation by pre-calculating the offsets between two intersections. Such
pre-calculated offsets are not suitable for dynamic traffic environments. To
enable cooperation of traffic signals, in this paper, we propose a model,
CoLight, which uses graph attentional networks to facilitate communication.
Specifically, for a target intersection in a network, CoLight can not only
incorporate the temporal and spatial influences of neighboring intersections to
the target intersection, but also build up index-free modeling of neighboring
intersections. To the best of our knowledge, we are the first to use graph
attentional networks in the setting of reinforcement learning for traffic
signal control and to conduct experiments on the large-scale road network with
hundreds of traffic signals. In experiments, we demonstrate that by learning
the communication, the proposed model can achieve superior performance against
the state-of-the-art methods.Comment: 10 pages. Proceedings of the 28th ACM International on Conference on
Information and Knowledge Management. ACM, 201
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning
Recent advances in combining deep neural network architectures with
reinforcement learning techniques have shown promising potential results in
solving complex control problems with high dimensional state and action spaces.
Inspired by these successes, in this paper, we build two kinds of reinforcement
learning algorithms: deep policy-gradient and value-function based agents which
can predict the best possible traffic signal for a traffic intersection. At
each time step, these adaptive traffic light control agents receive a snapshot
of the current state of a graphical traffic simulator and produce control
signals. The policy-gradient based agent maps its observation directly to the
control signal, however the value-function based agent first estimates values
for all legal control signals. The agent then selects the optimal control
action with the highest value. Our methods show promising results in a traffic
network simulated in the SUMO traffic simulator, without suffering from
instability issues during the training process
Approaches for Future Internet architecture design and Quality of Experience (QoE) Control
Researching a Future Internet capable of overcoming the current Internet limitations is a strategic
investment. In this respect, this paper presents some concepts that can contribute to provide some guidelines to
overcome the above-mentioned limitations. In the authors' vision, a key Future Internet target is to allow
applications to transparently, efficiently and flexibly exploit the available network resources with the aim to
match the users' expectations. Such expectations could be expressed in terms of a properly defined Quality of
Experience (QoE). In this respect, this paper provides some approaches for coping with the QoE provision
problem
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