Abstract
This paper investigates the use of multi-agent deep Q-network (MADQN) to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning (MARL) approach. The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions, particularly rainfall. MADQN is based on deep Q-network (DQN), which is an integration of the traditional reinforcement learning (RL) and the newly emerging deep learning (DL) approaches. MADQN enables traffic light controllers to learn, exchange knowledge with neighboring agents, and select optimal joint actions in a collaborative manner. A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in Malaysia. Investigation is also performed using a grid traffic network (GTN) to understand that the proposed scheme is effective in a traditional traffic network. Our proposed scheme is evaluated using two simulation tools, namely Matlab and Simulation of Urban Mobility (SUMO). Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30% in the simulations.
Highlights
Traffic congestion has become a problem in most urban areas of the world, causing enormous economic waste, extra travel delay, and excessive vehicle emission [1]
We aim to show a performance comparison between multi-agent deep Q-network (MADQN) and multi-agent reinforcement learning (MARL) applied to traffic light controllers at intersections with a high volume of traffic and traffic disruptions in terms of the cumulative delay of vehicles, which has not been investigated in the literature despite its significance
As compared to MARL, the accumulated delayed reward achieved by MADQN is higher in both types of traffic congestions (i.e., RC and NRC) and traffic networks (i.e., Sunway city traffic network (SCTN) and grid traffic network (GTN))
Summary
Traffic congestion has become a problem in most urban areas of the world, causing enormous economic waste, extra travel delay, and excessive vehicle emission [1]. CMC, 2022, vol., no.2 of red lights for all lanes to provide a safe transition in between traffic phases. The time passed since the respective lane light has changed to red is represented by red timing. We present the background of the Krauss vehicle-following model, DRL and MARL. The Krauss vehicle-following model is a mathematical model of safe vehicular movement, whereby a gap between two consecutive vehicles is maintained. The background of DRL includes the traditional single-agent deep Q-network (DQN) algorithm. The background of MARL includes its traditional algorithm. In 1997, Krauss developed a vehicle-following model based on the safe speed of vehicles. Where ul (t) and uf (t) represent the speed of the leading and following vehicles at time t, respectively, and g (t) is the gap to the leading vehicle at time t. The driver’s reaction time (e.g., one second) is represented by τr, and b is the maximum deceleration of the vehicle
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