Variations in Multi-Agent Actor-Critic Frameworks for Joint Optimizations in UAV Swarm Networks: Recent Evolution, Challenges, and Directions

TL;DR

This paper reviews recent developments in multi-agent actor-critic frameworks for joint optimization in UAV swarm networks, highlighting challenges and future research directions for dynamic, resource-constrained environments.

Contribution

It provides a comprehensive overview of recent evolution, challenges, and potential solutions for adaptive actor-critic frameworks in UAV swarm network optimization.

Findings

Recent actor-critic methods effectively handle joint optimization in UAVSNs.

Challenges include dynamic topology and resource limitations.

Future directions involve addressing scalability and real-time adaptation.

Abstract

Autonomous unmanned aerial vehicle (UAV) swarm networks (UAVSNs) can effectively execute surveillance, connectivity, and computing services to ground users (GUs). These missions require trajectory planning, UAV-GUs association, task offloading, next-hop selection, and resources such as transmit power, bandwidth, caching, and computing allocation to improve network performances. Owing to the highly dynamic topology, limited resources, and non-availability of global knowledge, optimizing network performance in UAVSNs is very intricate. Hence, it requires an adaptive joint optimization framework that can tackle both discrete and continuous decision variables to ensure optimal network performance under dynamic constraints. Multi-agent deep reinforcement learning-based adaptive actor-critic framework can efficiently address these problems. This paper investigates the recent evolutions of actor-critic frameworks to deal with joint optimization problems in UAVSNs. In addition, challenges and potential solutions are addressed as research directions.