Abstract — Optimal traffic light control is a multi-agent decision problem, for which we propose to use reinforcement learning algorithms. Our algorithm learns the expected wait-ing times of cars for red and green lights at each intersection, and sets the traffic lights to green for the configuration maximizing individual car gains. For testing our adaptive traffic light controllers, we developed the Green Light District simulator. The experimental results show that the adaptive algorithms can strongly reduce average waiting times of cars compared to three hand-designed controllers. I
4siWe consider a mixed autonomy scenario where the traffic intersection controller decides whether t...
We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic...
Optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for redu...
Abstract — Optimal traffic light control is a multi-agent decision problem, for which we propose to ...
Due to the increasing amounts of traffic in and around urban areas there is a growing need for intel...
Traffic light control is one of the main means of controlling road traffic. Improving traffic contro...
Traffic light control is one of the main means of controlling road traffic. Improving traffic contro...
This paper describes using multi-agent rein-forcement learning (RL) algorithms for learn-ing traÆc l...
Using some sort of adaptive traffic light control system is becoming standard policy among metropoli...
Devanshi Malaviya, CIS494: Research in Computer Information SystemsFaculty Mentor(s): Professor Sarb...
<p>The basic principle of optimal traffic control is the appropriate real-time response to dynamic t...
Recent advances in combining deep neural network architectures with reinforcement learning (RL) tech...
The rapid growth of urbanization and the constant demand for mobility have put a great strain on tra...
The aim of this thesis was to explore the possibilities of using deep reinforcement learning in traf...
Recent advances in combining deep neural network architectures with reinforcement learning (RL) tech...
4siWe consider a mixed autonomy scenario where the traffic intersection controller decides whether t...
We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic...
Optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for redu...
Abstract — Optimal traffic light control is a multi-agent decision problem, for which we propose to ...
Due to the increasing amounts of traffic in and around urban areas there is a growing need for intel...
Traffic light control is one of the main means of controlling road traffic. Improving traffic contro...
Traffic light control is one of the main means of controlling road traffic. Improving traffic contro...
This paper describes using multi-agent rein-forcement learning (RL) algorithms for learn-ing traÆc l...
Using some sort of adaptive traffic light control system is becoming standard policy among metropoli...
Devanshi Malaviya, CIS494: Research in Computer Information SystemsFaculty Mentor(s): Professor Sarb...
<p>The basic principle of optimal traffic control is the appropriate real-time response to dynamic t...
Recent advances in combining deep neural network architectures with reinforcement learning (RL) tech...
The rapid growth of urbanization and the constant demand for mobility have put a great strain on tra...
The aim of this thesis was to explore the possibilities of using deep reinforcement learning in traf...
Recent advances in combining deep neural network architectures with reinforcement learning (RL) tech...
4siWe consider a mixed autonomy scenario where the traffic intersection controller decides whether t...
We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic...
Optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for redu...