The computing in the network (COIN) paradigm is a promising solution that leverages unused network resources to perform tasks to meet computation-demanding applications, such as the metaverse. In this vein, we consider the partial computation offloading problem in the metaverse for multiple subtasks in a COIN environment to minimize energy consumption and delay while dynamically adjusting the offloading policy based on the changing computational resource status. The problem is NP-hard, and we transform it into two subproblems: the task-splitting problem (TSP) on the user side and the task-offloading problem (TOP) on the COIN side. We model the TSP as an ordinal potential game and propose a decentralized algorithm to obtain its Nash equilibrium (NE). Then, we model the TOP as a Markov decision process and propose the double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, the COIN agent explores the NE of the TSP and the deep neural network. Finally, the simulation results reveal that the proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline

翻译：网络内计算（COIN）范式是一种有前景的解决方案，它利用未被占用的网络资源执行任务，以满足元宇宙等计算密集型应用的需求。在此背景下，我们研究了元宇宙中多个子任务在COIN环境下的部分计算卸载问题，旨在根据不断变化的计算资源状态动态调整卸载策略，以最小化能耗和延迟。该问题属于NP难问题，我们将其转化为两个子问题：用户端的任务分割问题（TSP）和COIN端的任务卸载问题（TOP）。我们将TSP建模为序数势博弈，并提出一种去中心化算法以获取其纳什均衡（NE）。随后，将TOP建模为马尔可夫决策过程，并采用双深度Q网络（DDQN）求解最优卸载策略。与传统DDQN算法中智能体以固定概率随机采样卸载决策不同，COIN智能体通过探索TSP的纳什均衡与深度神经网络进行决策。最后，仿真结果表明，所提出的模型方法使COIN智能体能够更新策略并做出更优决策，相较于传统基线方法，性能随时间推移持续提升。