Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling

TL;DR

This paper introduces a Digital Twin-assisted reinforcement learning approach to enhance task scheduling efficiency at edge servers, addressing NP-hardness and slow convergence issues in traditional methods.

Contribution

It proposes two novel DT-assisted RL algorithms, DTAQL and DTEQL, that significantly improve exploration efficiency and convergence speed in edge task scheduling.

Findings

Both algorithms outperform traditional RL in convergence speed.

Simulation results confirm improved exploration efficiency.

Enhanced scheduling performance reduces computational complexity.

Abstract

Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmitted to server at a time, while server processes tasks according to the transmission order, the problem is NP-hard. However, it is difficult for traditional optimization methods to quickly obtain the optimal solution, while approaches based on reinforcement learning face with the challenge of excessively large action space and slow convergence. In this paper, we propose a Digital Twin (DT)-assisted RL-based task scheduling method in order to improve the performance and convergence of the RL. We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT. In this way, the exploration efficiency of RL can be significantly improved via DT, and thus RL can converges faster and local optimality is less likely to happen. Particularly, two algorithms are designed to made task scheduling decisions, i.e., DT-assisted asynchronous Q-learning (DTAQL) and DT-assisted exploring Q-learning (DTEQL). Simulation results show that both algorithms significantly improve the convergence speed of Q-learning by increasing the exploration efficiency.