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Joint velocity and spectrum optimization in urban air transportation system via multi-agent deep reinforcement learning
The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open
a new paradigm for urban air transportation. One big challenge is that new aerial vehicles …
a new paradigm for urban air transportation. One big challenge is that new aerial vehicles …
Deep reinforcement learning based resource allocation in delay-tolerance-aware 5G Industrial IoT systems
With the widespread application of 5G technology in the Industrial Internet of Things (IIoT),
dividing nodes into different network slices according to delay tolerance requirements can …
dividing nodes into different network slices according to delay tolerance requirements can …
An adaptive route guidance model considering the effect of traffic signals based on deep reinforcement learning
Navigation or route guidance systems are designed to provide drivers with real-time travel
information and the associated recommended routes for their trips. Classical route choice …
information and the associated recommended routes for their trips. Classical route choice …
Noncooperative and cooperative urban intelligent systems: joint logistic and charging incentive mechanisms
Autonomous vehicles (AVs) have become an emerging crucial component of the intelligent
transportation system (ITS) in modern smart cities. In particular, coordinated operations of …
transportation system (ITS) in modern smart cities. In particular, coordinated operations of …
Dynamic and effect-driven output service selection for IoT environments using deep reinforcement learning
In the context of the recent emergence of the Internet of Things (IoT), human users and IoT-
based services are interacting via physical effects, such as light and sound. Therefore, it is …
based services are interacting via physical effects, such as light and sound. Therefore, it is …
Distributed Multi-Agent Reinforcement Learning for Collaborative Path Planning and Scheduling in Blockchain-Based Cognitive Internet of Vehicles
The collaborative path planning and scheduling can overcome the limitations of single
vehicle intelligence to obtain a globally optimal decision strategy in cognitive Internet of …
vehicle intelligence to obtain a globally optimal decision strategy in cognitive Internet of …
FedCruise: Collaborative Cruise Guidance With Federated Policy Distillation in Multiple Ride-Hailing Platforms
Recent technological advancements have led to the emergence of intelligent cruise
guidance systems tailored for ride-hailing platforms (RHPs) such as Uber, Didi Chuxing …
guidance systems tailored for ride-hailing platforms (RHPs) such as Uber, Didi Chuxing …
Large Vehicle Scheduling Based on Uncertainty Weighting Harmonic Twin-Critic Network
X Huang, K Yang, J Ling - IEEE Systems Journal, 2023 - ieeexplore.ieee.org
Large-scale online car-hailing platforms have greatly improved travel efficiency by
dispatching orders quickly. However, effectively scheduling vehicles for a large-scale fleet is …
dispatching orders quickly. However, effectively scheduling vehicles for a large-scale fleet is …
D4: Dynamic, Decentralized, Distributed, Delegation-Based Network Control and Its Applications to Autonomous Vehicles
Connected autonomous vehicles technology is expected to be an important component of
Intelligent Transportation Systems (ITS). With the help of artificial intelligence, cognitive …
Intelligent Transportation Systems (ITS). With the help of artificial intelligence, cognitive …
Reinforcement learning assisted communication resources optimization in advanced air mobility.
R Han - 2024 - ir.library.louisville.edu
Advanced air mobility (AAM), which envisages a safe and efficient aviation transportation
system, has drawn significant attention to support the increasing mobility demand in …
system, has drawn significant attention to support the increasing mobility demand in …