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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
Deep reinforcement learning for traffic signal control under disturbances: A case study on Sunway city, Malaysia
ترجمه فارسی عنوان مقاله:
یادگیری تقویتی عمیق برای کنترل سیگنال ترافیک تحت اختلالات: یک مطالعه موردی در شهر سان وی ، مالزی
منبع:
Sciencedirect - Elsevier - Future Generation Computer Systems, 109 (2020) 431-445. doi:10.1016/j.future.2020.03.065
نویسنده:
Faizan Rasheed, Kok-Lim Alvin Yau ∗, Yeh-Ching Low
چکیده انگلیسی:
In most urban areas, traffic congestion is a vexing, complex and growing issue day by day. Reinforcement
learning (RL) enables a single decision maker (or an agent) to learn and make optimal
actions in an independent manner, while multi-agent reinforcement learning (MARL) enables multiple
agents to exchange knowledge, learn, and make optimal joint actions in a collaborative manner.
The integration of the newly emerging deep learning and the traditional RL approach has created
an advanced technique called deep Q-network (DQN) that has shown promising results in solving
high-dimensional and complex problems, including traffic congestion. In this paper, DQN is embedded
in traffic signal control to solve traffic congestion issue, which has been plagued with the curse of
dimensionality whereby the representation of the operating environment can be highly dimensional
and complex when the traditional RL approach is used. Most importantly, this paper proposes multiagent
DQN (MADQN) and investigates its use to further address the curse of dimensionality under
traffic network scenarios with high traffic volume and disturbances. To investigate the effectiveness
of our proposed scheme, a case study based on an urban area, namely Sunway city in Malaysia, is
conducted. We evaluate our scheme via simulation using a traffic network simulator called simulation
of urban mobility (SUMO) and a simulation tool called MATLAB. Simulation results show that our
proposed scheme reduces the total travel time of the vehicles.
Keywords: Reinforcement learning | Deep reinforcement learning | Multi-agent reinforcement learning | Deep Q-network | Multi-agent deep Q-network | Traffic signal control
قیمت: رایگان
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