Green Resource Allocation with DDPG for Knowledge Learning in Digital Twin-enabled Edges

In the era of Information and Communication, big data is rapidly generated due to the increasing data-driven applications in Internet of Things (IoT). Effectively processing such data, e.g., knowledge learning, on resource-limited IoT becomes a challenge. In this paper, we introduce a digital twin-enabled IoT, in order to achieve hyper-connected experience, green communication, and sustainable computing. Although knowledge learning benefits from the proposed system, system latency and energy consumption are still our focus in the distributed learning architecture. To this end, we leverage Deep Reinforcement Learning (DRL) to present the deep deterministic policy gradient with double actors and double critics (D
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PG) to manage the multi-dimensional resources, i.e., CPU cycles, DT models, and communication bandwidths, enhancing the exploration ability and improving the inaccurate value estimation of agents in continuous action spaces. Extensive experimental results prove that the proposed architecture can efficiently conduct knowledge learning, and our intelligent scheme can effectively improve the system efficiency.