论文标题
Markovian神经网络和深层随机控制的Langevin算法
Langevin algorithms for Markovian Neural Networks and Deep Stochastic control
论文作者
论文摘要
在某些情况下，在神经网络非常深的情况下，众所周知，随机梯度下降Langevin动力学（SGLD）算法可为经典梯度下降增加噪声。在本文中，我们研究了通过梯度下降来解决随机控制问题的训练加速度的可能性，其中控制由神经网络参数化。如果在许多离散的时间内应用控制，则解决随机控制问题可以减少以最大程度地减少非常深的神经网络的损失。我们从数值上表明，Langevin算法改善了各种随机控制问题（例如对冲和资源管理）以及梯度下降方法的不同选择。
Stochastic Gradient Descent Langevin Dynamics (SGLD) algorithms, which add noise to the classic gradient descent, are known to improve the training of neural networks in some cases where the neural network is very deep. In this paper we study the possibilities of training acceleration for the numerical resolution of stochastic control problems through gradient descent, where the control is parametrized by a neural network. If the control is applied at many discretization times then solving the stochastic control problem reduces to minimizing the loss of a very deep neural network. We numerically show that Langevin algorithms improve the training on various stochastic control problems like hedging and resource management, and for different choices of gradient descent methods.
