论文标题
部分可观测时空混沌系统的无模型预测
Distributed Training for Deep Learning Models On An Edge Computing Network Using ShieldedReinforcement Learning
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Edge devices with local computation capability has made distributed deep learning training on edges possible. In such method, the cluster head of a cluster of edges schedules DL training jobs from the edges. Using such centralized scheduling method, the cluster head knows all loads of edges, which can avoid overloading the cluster edges, but the head itself may become overloaded. To handle this problem, we propose a multi-agent RL (MARL) system that enables each edge to schedule its jobs using RL. However, without coordination among edges, action collision may occur, in which multiple edges schedule tasks to the same edge and make it overloaded. For this reason, we propose a system called Shielded ReinfOrcement learning (RL) based DL training on Edges (SROLE). In SROLE, the shield deployed in an edge checks action collisions and provides alternative actions to avoid collisions. As the central shield for entire cluster may become a bottleneck, we further propose a decentralized shielding method, where different shields are responsible for different regions in the cluster and they coordinate to avoid action collisions on the region boundaries. Our emulation and real device experiments show SROLE reduces training time by 59% compared to MARL and centralized RL.
