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With the blooming of information technology and network applications/services, emerging multi-access edge computing (MEC) and in-network computing (INC) are regarded as key computing paradigms to support time-sensitive tasks and requests by task offloading. Existing studies concerning task offloading seldom considered the combinations of MEC, INC, and cloud computing. In this paper, we explore INC-enhanced task offloading in MEC networks and design a three-layer task offloading network architecture, which consists of not only user equipment, edge servers, and cloud, but also network elements like routers/switches. We focus on reducing the system latency and energy consumption (EC) and formulate the optimization problem as minimizing the weighted sum of these two indicators. To solve this problem, we propose a deep reinforcement learning-based framework and creatively map the actions of …