Large-scale traffic control using autonomous vehicles and decentralized deep reinforcement learning
In this work, we introduce a scalable, decentralized deep reinforcement learning (RL)
scheme for optimizing vehicle traffic consisting of both autonomous and human-driven …
scheme for optimizing vehicle traffic consisting of both autonomous and human-driven …
An efficiency enhancing methodology for multiple autonomous vehicles in an Urban network adopting deep reinforcement learning
QD Tran, SH Bae - Applied Sciences, 2021 - mdpi.com
To reduce the impact of congestion, it is necessary to improve our overall understanding of
the influence of the autonomous vehicle. Recently, deep reinforcement learning has become …
the influence of the autonomous vehicle. Recently, deep reinforcement learning has become …
Deep reinforcement learning based traffic signal optimization for multiple intersections in ITS
A Paul, S Mitra - 2020 IEEE International Conference on …, 2020 - ieeexplore.ieee.org
The number of vehicles is drastically increasing worldwide, especially in large cities. Thus
there is a need to model and enhance the traffic management to help meet this rising …
there is a need to model and enhance the traffic management to help meet this rising …
Towards a very large scale traffic simulator for multi-agent reinforcement learning testbeds
Smart traffic control and management become an emerging application for Deep
Reinforcement Learning (DRL) to solve traffic congestion problems in urban networks …
Reinforcement Learning (DRL) to solve traffic congestion problems in urban networks …
Optimizing mixed autonomy traffic flow with decentralized autonomous vehicles and multi-agent rl
We study the ability of autonomous vehicles to improve the throughput of a bottleneck using
a fully decentralized control scheme in a mixed autonomy setting. We consider the problem …
a fully decentralized control scheme in a mixed autonomy setting. We consider the problem …
Traffic smoothing controllers for autonomous vehicles using deep reinforcement learning and real-world trajectory data
Designing traffic-smoothing cruise controllers that can be deployed onto autonomous
vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing …
vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing …
Optimizing mixed autonomy traffic flow with decentralized autonomous vehicles and multi-agent reinforcement learning
We study the ability of autonomous vehicles to improve the throughput of a bottleneck using
a fully decentralized control scheme in a mixed autonomy setting. We consider the problem …
a fully decentralized control scheme in a mixed autonomy setting. We consider the problem …
Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing
Deep Reinforcement Learning (DRL) uses diverse, unstructured data and makes RL
capable of learning complex policies in high dimensional environments. Intelligent …
capable of learning complex policies in high dimensional environments. Intelligent …
Vehicle control in highway traffic by using reinforcement learning and microscopic traffic simulation
The paper presents a simple yet powerful and intelligent driver agent, designed to operate in
a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. The …
a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. The …
Learning to control and coordinate mixed traffic through robot vehicles at complex and unsignalized intersections
Intersections are essential road infrastructures for traffic in modern metropolises. However,
they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of …
they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of …
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