… transportation applications. In this paper, the latest deep reinforcement learning (RL) based
traffic … Specifically, traffic signal control (TSC) applications based on (deep) RL, which have …

… We have explored the scope for Deep Q-Networks (DQN) to optimize real-time traffic light
control policies in emerging large-scale Intelligent Transportation Systems. As an initial …

… efficiency of the traffic light control system, in this work, a Dynamic and Intelligent Traffic
Light Control System (DITLCS) is proposed which takes real-time traffic information as the input …

… a myriad of power and transportation network data for … traffic conditions, charging prices
and waiting time at EV charging station (EVCS). In this paper, we propose a deep reinforcement …

… the traffic conditions and it generates the traffic light control signal (Red/Yellow/Green)
accordingly. For an intelligent traffic light control signal, a Deep Reinforcement … the real-time traffic …

… Shiratori, “Adaptive traffic signal control: Deep reinforcement learning algorithm with
experience replay and target network,” 2017, arXiv:1705.02755. [Online]. Available: https://arxiv.org/…

… of traffic congestion and its adverse effect on smart cities. Edge … -based deep reinforcement
learning system is demonstrated … The system seeks to overcome the challenge of evaluating …

… research efforts have investigated how to reduce the unpredictability by using a combination
of models with multiple layers, deep architectural models, and deep reinforcement models. …

… Deep reinforcement learning (DRL) is an approach to solving the RL problem using a DNN.
Although the history of DRL began in the 1990s when Tesauro (1995) developed a neural …

… areas like intelligent transportation system and Industry 4.0. We do not list smart city, an …
application areas here: healthcare, intelligent transportation system, smart grid, etc. We do …