The integration of these algorithms constitutes the overall MAARS framework, which leverages a multiagent system based on the actor–critic structure to efficiently manage network resources, thereby optimizing overall performance. It aims to maximize resource utilization while minimizing resource conflicts ...
Machine learning has proven invaluable across various domains, including vehicular communications, offering optimal solutions through advanced algorithms. Several studies have employed machine learning to address challenges in V2X communications, such as optimizing resource allocation in C-V2X Mode 4 to ...
Soft-policy iteration algorithms alternate the soft-policy evaluation and soft-policy improvement steps. The detailed derivation process of the algorithm can be found in the references [41,42]. 2.3. Element Configuration The tunable reflective metasurface arrangement is composed of a group of unit cel...
Simulation results have demonstrated that our method has significant advantages over traditional methods and other deep-learning algorithms, and effectively improves the communication performance of NOMA transmission to some extent. Keywords: NOMA; deep-reinforcement learning; actor–critic; power allocation;...
Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms. Keywords: wireless rechargeable sensor network; deep reinforcement learning; multi-agent; attention-shared; ...