Hyperdimensional Hybrid Learning on End-Edge-Cloud Networks

In this paper, we present Hyperdimensional Hybrid Learning (HDHL), which combines model-free and model-based Reinforcement Learning, to effectively reduce the computational cost and environment interaction for optimizing an intelligent cloud service. We first show that Hyperdimensional Q-Learning (QHD), the state-of-the-art Hyperdimensional Computing value-based Reinforcement Learning algorithm, is computationally faster than the Deep Q-Network (DQN) for this task. In addition, we demonstrate how HDHL reduces the number of environment interactions by 4.8× to learn the near optimal configuration. Our evaluation shows that HDHL is computationally more efficient than both Q-Learning algorithms, with the total time being reduced by 21.0× compared to DQN and 16.5× compared to QHD.