Robust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multi-Agent DRL Approach

TL;DR

This paper presents a robust multi-agent deep reinforcement learning approach to optimize UAV trajectories and resource allocation in multi-UAV-assisted MEC networks, effectively handling uncertainties in communication and computation.

Contribution

It introduces a novel multi-agent DRL framework with Beta distribution for robust optimization in uncertain multi-UAV MEC environments, outperforming existing benchmarks.

Findings

The proposed algorithm reduces energy consumption effectively.

It demonstrates robustness against communication and computation uncertainties.

Outperforms benchmark algorithms in numerical simulations.

Abstract

For multiple Unmanned-Aerial-Vehicles (UAVs) assisted Mobile Edge Computing (MEC) networks, we study the problem of combined computation and communication for user equipments deployed with multi-type tasks. Specifically, we consider that the MEC network encompasses both communication and computation uncertainties, where the partial channel state information and the inaccurate estimation of task complexity are only available. We introduce a robust design accounting for these uncertainties and minimize the total weighted energy consumption by jointly optimizing UAV trajectory, task partition, as well as the computation and communication resource allocation in the multi-UAV scenario. The formulated problem is challenging to solve with the coupled optimization variables and the high uncertainties. To overcome this issue, we reformulate a multi-agent Markov decision process and propose a multi-agent proximal policy optimization with Beta distribution framework to achieve a flexible learning policy. Numerical results demonstrate the effectiveness and robustness of the proposed algorithm for the multi-UAV-assisted MEC network, which outperforms the representative benchmarks of the deep reinforcement learning and heuristic algorithms.