Applications of multi-agent reinforcement learning in future internet: A comprehensive survey
Future Internet involves several emerging technologies such as 5G and beyond 5G
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
RIS-assisted UAV communications for IoT with wireless power transfer using deep reinforcement learning
Many of the devices used in Internet-of-Things (IoT) applications are energy-limited, and
thus supplying energy while maintaining seamless connectivity for IoT devices is of …
thus supplying energy while maintaining seamless connectivity for IoT devices is of …
3D UAV trajectory and data collection optimisation via deep reinforcement learning
Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the
network performance and coverage in wireless communication. However, due to the …
network performance and coverage in wireless communication. However, due to the …
DDPG-driven deep-unfolding with adaptive depth for channel estimation with sparse Bayesian learning
Deep-unfolding neural networks (NNs) have received great attention since they achieve
satisfactory performance with relatively low complexity. Typically, these deep-unfolding NNs …
satisfactory performance with relatively low complexity. Typically, these deep-unfolding NNs …
A deep deterministic policy gradient learning approach to missile autopilot design
In this paper a Deep Reinforcement Learning algorithm, known as Deep Deterministic Policy
Gradient (DDPG), is applied to the problem of designing a missile lateral acceleration …
Gradient (DDPG), is applied to the problem of designing a missile lateral acceleration …
Joint spectrum allocation and power control in vehicular communications based on dueling double DQN
J Ren, Z Chai, Z Chen - Vehicular Communications, 2022 - Elsevier
Vehicular communications is one of the important applications of 5G communication
systems. The allocation scheme of scarce wireless resources will directly affect the quality of …
systems. The allocation scheme of scarce wireless resources will directly affect the quality of …
Power allocation in ultra-dense networks through deep deterministic policy gradient
J Liu, H Zhang - IEEE Wireless Communications Letters, 2022 - ieeexplore.ieee.org
The existing reinforcement learning-based downlink power allocation (PA) schemes mostly
consider the power optimization space as a discrete value space, however, their results will …
consider the power optimization space as a discrete value space, however, their results will …
Simulating multi-agent-based computation offloading for autonomous cars
Efficient task processing and data storage are still among the most important challenges in
Autonomous Driving (AD). In-board processing units struggle to deal with the workload of …
Autonomous Driving (AD). In-board processing units struggle to deal with the workload of …
Fuzzy deep deterministic policy gradient-based motion controller for humanoid robot
PH Kuo, J Hu, ST Lin, PW Hsu - International Journal of Fuzzy Systems, 2022 - Springer
In conventional robot arm control, inverse kinematics (IK) is used as the basis for computing
arm joint angles. However, IK can be used to compute joint angles only after the terminal …
arm joint angles. However, IK can be used to compute joint angles only after the terminal …
Deep reinforcement learning based resource allocation for D2D communications underlay cellular networks
S Yu, JW Lee - Sensors, 2022 - mdpi.com
In this paper, a resource allocation (RA) scheme based on deep reinforcement learning
(DRL) is designed for device-to-device (D2D) communications underlay cellular networks …
(DRL) is designed for device-to-device (D2D) communications underlay cellular networks …