Browsing by Author "Bouktif, Salah"

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Traffic signal control based on deep reinforcement learning with simplified state and reward definitions
(IEEE, 2021) Bouktif, Salah; Cheniki, Abderraouf; Ouni, Ali; El-Sayed, Hesham
Traffic congestion has recently become a real issue especially within crowded cities and urban areas. Intelligent transportation systems (ITS) leveraged various advanced tech- niques aiming to optimize the traffic flow and subsequently alleviate the traffic congestion. In particular, traffic signal control TSC is one of the essential ITS techniques for controlling the traffic flow at intersections. Many research works have been proposed to develop algorithms and techniques which optimize TSC behavior. Recent works leverage Deep Learning (DL) and Reinforcement Learning (RL) techniques to optimize TSCs. However, most of Deep RL proposals are based on complex definitions of state and reward in the RL framework. In this work, we propose to use an alternative way of formulating the state and reward definitions. Basically, The basic idea is to define both state and reward in a simplified and straightforward manner rather than the complex design. We hypothesize that such a design approach simplifies the learning of the RL agent and hence provides a rapid convergence to optimal policies. For the agent architecture, we employ the double deep Q-Network (DDQN) along with prioritized experience replay (PER). We conduct the experiments using the Simulation of Urban MObility (SUMO) simulator interfaced with Python framework and we compare the performance of our proposal to traditional and learning-based techniques
Traffic signal control using hybrid action space deep reinforcement learning
(MDPI AG, 2021) Bouktif, Salah; Cheniki, Abderraouf; Ouni, Ali
Recent research works on intelligent traffic signal control (TSC) have been mainly focused on leveraging deep reinforcement learning (DRL) due to its proven capability and performance. DRL-based traffic signal control frameworks belong to either discrete or continuous controls. In discrete control, the DRL agent selects the appropriate traffic light phase from a finite set of phases. Whereas in continuous control approach, the agent decides the appropriate duration for each signal phase within a predetermined sequence of phases. Among the existing works, there are no prior approaches that propose a flexible framework combining both discrete and continuous DRL approaches in controlling traffic signal. Thus, our ultimate objective in this paper is to propose an approach capable of deciding simultaneously the proper phase and its associated duration. Our contribution resides in adapting a hybrid Deep Reinforcement Learning that considers at the same time discrete and continuous decisions. Precisely, we customize a Parameterized Deep Q-Networks (P-DQN) architecture that permits a hierarchical decision-making process that primarily decides the traffic light next phases and secondly specifies its the associated timing. The evaluation results of our approach using Simulation of Urban MObility (SUMO) shows its out-performance over the benchmarks. The proposed framework is able to reduce the average queue length of vehicles and the average travel time by 22.20% and 5.78%, respectively, over the alternative DRL-based TSC systems