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Task scheduling is a critical problem when one user offloads multipledifferent tasks to the edge server. When a user has multiple tasks to offloadand only one task can be transmitted to server at a time, while serverprocesses tasks according to the transmission order, the problem is NP-hard.However, it is difficult for traditional optimization methods to quickly obtainthe optimal solution, while approaches based on reinforcement learning facewith the challenge of excessively large action space and slow convergence. Inthis paper, we propose a Digital Twin (DT)-assisted RL-based task schedulingmethod in order to improve the performance and convergence of the RL. We use DTto simulate the results of different decisions made by the agent, so that oneagent can try multiple actions at a time, or, similarly, multiple agents caninteract with environment in parallel in DT. In this way, the explorationefficiency of RL can be significantly improved via DT, and thus RL canconverges 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 exploringQ-learning (DTEQL). Simulation results show that both algorithms significantlyimprove the convergence speed of Q-learning by increasing the explorationefficiency.

Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling