To support future 6G mobile applications, the mobile edge computing (MEC) network needs to be jointly optimized for computing, pushing, and caching to reduce transmission load and computation cost. To achieve this, we propose a framework based on deep reinforcement learning that enables the dynamic orchestration of these three activities for the MEC network. The framework can implicitly predict user future requests using deep networks and push or cache the appropriate content to enhance performance. To address the curse of dimensionality resulting from considering three activities collectively, we adopt the soft actor-critic reinforcement learning in continuous space and design the action quantization and correction specifically to fit the discrete optimization problem. We conduct simulations in a single-user single-server MEC network setting and demonstrate that the proposed framework effectively decreases both transmission load and computing cost under various configurations of cache size and tolerable service delay.

翻译：为支持未来6G移动应用，移动边缘计算（MEC）网络需对计算、推送与缓存进行联合优化，以降低传输负载与计算成本。为此，我们提出一种基于深度强化学习的框架，能够动态编排MEC网络中这三类活动。该框架利用深度网络隐式预测用户未来请求，主动推送或缓存适当内容以提升性能。针对三类活动联合优化带来的维度灾难问题，我们采用连续空间中的软演员-评论家强化学习方法，并专门设计了动作量化与修正机制以适应离散优化问题。我们在单用户单服务器MEC网络场景下进行仿真，结果表明所提框架在不同缓存容量与可容忍服务延迟配置下，均能有效降低传输负载与计算成本。