Rainbow Deep Reinforcement Learning Agent for Improved Solution of the Traffic Congestion

Nawar, Mahmoud; Fares, Ahmed; Alsammak, Abdulwahab K.

doi:10.1109/jac-ecc48896.2019.9051262

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…Recently, many studies have explored DRL-based approaches, such as Rainbow [8], DDPG [9], AC [10], A2C [11], A3C [12], PPO [13], and DQN [14]. Among these, deep Q learning has been proven to be capable of handling realistic situations with complex road geometries and traffic scenarios.…”

Section: Methodsmentioning

confidence: 99%

Enhancing the Robustness of Traffic Signal Control with StageLight: A Multiscale Learning Approach

Su,

Yang

2024

Eng

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The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area that has garnered significant interest from both researchers and practitioners is the application of deep reinforcement learning (DRL) in traffic signal control. However, DRL-based algorithms often suffer from instability due to the dynamic nature of traffic flows. Discrepancies between the environments used for training and those encountered during deployment often lead to operational failures. Moreover, conventional DRL-based traffic signal control algorithms tend to reveal vulnerabilities when faced with unforeseen events, such as sensor failure. These challenges highlight the need for innovative solutions to enhance the robustness and adaptability of such systems. To address these pertinent issues, this paper introduces StageLight, a novel two-stage multiscale learning approach, which involves learning optimal timings on a coarse time scale in stage 1, while finetuning them on a finer time scale in stage 2. Our experimental results demonstrate StageLight’s remarkable capability to generalize across diverse traffic conditions and its robustness to various sensor-failure scenarios.

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Section: Methodsmentioning

confidence: 99%

Enhancing the Robustness of Traffic Signal Control with StageLight: A Multiscale Learning Approach

Su,

Yang

2024

Eng

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“…Compared with other algorithms, Rainbow DQN has achieved noticeable improvements in the reward and convergence speed. Given its great capabilities, Rainbow DQN has been widely applied in open car simulation (Güçkıran & Bolat, 2019), adaptive traffic signals (Nawar, Fares, & Al‐Sammak, 2019), and predictive panoramic video streaming (Xiao, Wu, Shi, Zhou, & Chen, 2019).…”

Section: Related Workmentioning

confidence: 99%

Simulating multi‐exit evacuation using deep reinforcement learning

Huang

Mango

et al. 2021

Transactions in GIS

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Conventional simulations on multi‐exit indoor evacuation focus primarily on how to determine a reasonable exit based on numerous factors in a changing environment. Results commonly include some congested and other under‐utilized exits, especially with large numbers of pedestrians. We propose a multi‐exit evacuation simulation based on deep reinforcement learning (DRL), referred to as the MultiExit‐DRL, which involves a deep neural network (DNN) framework to facilitate state‐to‐action mapping. The DNN framework applies Rainbow Deep Q‐Network (DQN), a DRL algorithm that integrates several advanced DQN methods, to improve data utilization and algorithm stability and further divides the action space into eight isometric directions for possible pedestrian choices. We compare MultiExit‐DRL with two conventional multi‐exit evacuation simulation models in three separate scenarios: varying pedestrian distribution ratios; varying exit width ratios; and varying open schedules for an exit. The results show that MultiExit‐DRL presents great learning efficiency while reducing the total number of evacuation frames in all designed experiments. In addition, the integration of DRL allows pedestrians to explore other potential exits and helps determine optimal directions, leading to a high efficiency of exit utilization.

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“…The previous research mainly focused on the design of the reward function with a single optimization objective, the optimization objective involved mainly includes delay, vehicle waiting time, queue length, number of stops, traffic volume, vehicle emissions, etc. [13,20,[27][28][29][30]. Given that urban traffic signal control needs to consider multi-objective optimization control problems such as safety, efficiency, and order [31], it is difficult to meet the application scenarios only with the improvement of a single objective as an action reward.…”

Section: Introductionmentioning

confidence: 99%

A Bus Signal Priority Control Method Based on Deep Reinforcement Learning

Shen

Zou

Deng³

et al. 2023

Applied Sciences

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To investigate the issue of multi-entry bus priority at intersections, an intelligent priority control method based on deep reinforcement learning was constructed in the bus network environment. Firstly, a dimension reduction method for the state vector based on the key lane was proposed, which contains characteristic parameters such as the bus states, the flow states, and the signal timing. Secondly, a control action method that can adjust phase sequence and phase green time at the same time was proposed under the constraints of maximum green and minimum green. Furthermore, a reward function, which can be uniformly converted into the number of standard cars, was established focusing on the indexes such as the busload and maximum waiting time. Finally, through building an experimental environment based on SUMO simulation, a real-time bus signal priority control method based on deep reinforcement learning was constructed. The results show that the algorithm can effectively reduce the waiting time of buses without affecting overall traffic efficiency. The findings can provide a theoretical basis for the signal control method considering bus priority and improve the operation efficiency of public transport.

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Rainbow Deep Reinforcement Learning Agent for Improved Solution of the Traffic Congestion

Cited by 8 publications

References 9 publications

Enhancing the Robustness of Traffic Signal Control with StageLight: A Multiscale Learning Approach

Enhancing the Robustness of Traffic Signal Control with StageLight: A Multiscale Learning Approach

Simulating multi‐exit evacuation using deep reinforcement learning

A Bus Signal Priority Control Method Based on Deep Reinforcement Learning

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