Intelligent Traffic Management(ITM)has progressively developed into a critical component of modern transportation networks,significantly enhancing traffic flow and reducing congestion in urban environments.This resear...Intelligent Traffic Management(ITM)has progressively developed into a critical component of modern transportation networks,significantly enhancing traffic flow and reducing congestion in urban environments.This research proposes an enhanced framework that leverages Deep Q-Learning(DQL),Game Theory(GT),and Stochastic Optimization(SO)to tackle the complex dynamics in transportation networks.The DQL component utilizes the distribution of traffic conditions for epsilon-greedy policy formulation and action and choice reward calculation,ensuring resilient decision-making.GT models the interaction between vehicles and intersections through probabilistic distributions of various features to enhance performance.Results demonstrate that the proposed framework is a scalable solution for dynamic optimization in transportation networks.展开更多
Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay ...Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the "curse of dimensionality" issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network;such a process is called experience replay.Heuristic knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.展开更多
With the rapid development of Internet of thing(IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtuali...With the rapid development of Internet of thing(IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization(NFV) technology with cloud computing and mobile edge computing(MEC), an NFV-enabled cloud-and-edge-collaborative IoT(CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain(SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping(DQLBM) algorithm as well as an energy-based topology adjustment(EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.展开更多
基金the Deanship of Scientific Research at King Khalid University for funding this research through the Large Group Research Project under grant number RGP2/324/46.
文摘Intelligent Traffic Management(ITM)has progressively developed into a critical component of modern transportation networks,significantly enhancing traffic flow and reducing congestion in urban environments.This research proposes an enhanced framework that leverages Deep Q-Learning(DQL),Game Theory(GT),and Stochastic Optimization(SO)to tackle the complex dynamics in transportation networks.The DQL component utilizes the distribution of traffic conditions for epsilon-greedy policy formulation and action and choice reward calculation,ensuring resilient decision-making.GT models the interaction between vehicles and intersections through probabilistic distributions of various features to enhance performance.Results demonstrate that the proposed framework is a scalable solution for dynamic optimization in transportation networks.
基金supported by the National Natural Science Foundation of China(61751210,61572441)。
文摘Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the "curse of dimensionality" issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network;such a process is called experience replay.Heuristic knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.
基金supported by the Science and Technology Project of State Grid Corporation of China(SGLNXT00GCJS2000160)。
文摘With the rapid development of Internet of thing(IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization(NFV) technology with cloud computing and mobile edge computing(MEC), an NFV-enabled cloud-and-edge-collaborative IoT(CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain(SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping(DQLBM) algorithm as well as an energy-based topology adjustment(EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.
