论文标题
分布式和分布式元加强学习（D2-RMRL）用于数据预处理和路由的数据集卫星网络
Distributed and Distribution-Robust Meta Reinforcement Learning (D2-RMRL) for Data Pre-storing and Routing in Cube Satellite Networks
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。
In this paper, the problem of data pre-storing and routing in dynamic, resource-constrained cube satellite networks is studied. In such a network, each cube satellite delivers requested data to user clusters under its coverage. A group of ground gateways will route and pre-store certain data to the satellites, such that the ground users can be directly served with the pre-stored data. This pre-storing and routing design problem is formulated as a decentralized Markov decision process (Dec-MDP) in which we seek to find the optimal strategy that maximizes the pre-store hit rate, i.e., the fraction of users being directly served with the pre-stored data. To obtain the optimal strategy, a distributed distribution-robust meta reinforcement learning (D2-RMRL) algorithm is proposed that consists of three key ingredients: value-decomposition for achieving the global optimum in distributed setting with minimum communication overhead, meta learning to obtain the optimal initial to reduce the training time under dynamic conditions, and pre-training to further speed up the meta training procedure. Simulation results show that, using the proposed value decomposition and meta training techniques, the satellite networks can achieve a 31.8% improvement of the pre-store hits and a 40.7% improvement of the convergence speed, compared to a baseline reinforcement learning algorithm. Moreover, the use of the proposed pre-training mechanism helps to shorten the meta-learning procedure by up to 43.7%.
