# Decentralized Computation Offloading Strategy via Multi-Agent Deep Reinforcement Learning for Multi-Access Edge Computing Systems

## TL;DR

This paper introduces a decentralized computation offloading strategy using multi-agent deep reinforcement learning to reduce task completion latency in edge computing systems.

## Contribution

The novel contribution is a decentralized MADRL-based offloading strategy with a grant-free access mechanism to minimize task completion latency in MEC systems.

## Key findings

- The proposed MADRL strategy reduces overall task completion latency compared to conventional schemes.
- The decentralized approach achieves faster learning convergence and better scalability in multi-user environments.
- The system dynamically adjusts offloading factors based on local observations and channel states.

## Abstract

Multi-access edge computing (MEC) has been widely recognized as a promising solution for alleviating the computational burden on edge devices, particularly in supporting fast and real-time processing of resource-intensive applications. In this paper, we propose a decentralized offloading decision strategy based on multi-agent deep reinforcement learning (MADRL), aiming to minimize the overall task completion latency experienced by edge devices. Our proposed scheme adopts a grant-free access mechanism during the initialization of offloading in a fully decentralized manner, which serves as the key feature of our strategy. As a result, determining the optimal offloading factor becomes significantly more challenging due to the simultaneous access attempts from multiple edge devices. To resolve this problem, we consider a discrete action space-based deep reinforcement learning (DRL) approach, termed deep Q network (DQN), to enable each edge device to learn a decentralized computation offloading policy based solely on its local observation without requiring global network information. In our design, each edge device dynamically adjusts its offloading factor according to its observed channel state and the number of active users, thereby balancing local and remote computation loads adaptively. Furthermore, the proposed MADRL-based framework jointly accounts for user association and offloading decision optimization to mitigate access collisions and computation bottlenecks in a multi-user environment. We perform extensive computer simulations using MATLAB R2023b to evaluate the performance of the proposed strategy, focusing on the task completion latency under various system configurations. The numerical results demonstrate that our proposed strategy effectively reduces the overall task completion latency and achieves faster convergence of learning performance compared with conventional schemes, confirming the efficiency and scalability of the proposed decentralized approach.