论文标题
Nesterov的准加速方法，用于使用深度强化学习的全球路线
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning
论文作者
论文摘要
深Q学习方法是使用深层神经网络近似作用值函数估计的最常用的深钢筋学习算法之一。深Q网络（DQN）的培训通常仅限于基于一阶梯度的方法。本文试图通过引入Nesterov加速的准Newton方法来加速对Q-Networks的训练。我们评估了使用双DQN进行全球路由的深入增强学习方法的性能。结果表明，与一阶Adam和RMSProp方法训练的DQN相比，所提出的方法可以获得更好的路由解决方案。
Deep Q-learning method is one of the most popularly used deep reinforcement learning algorithms which uses deep neural networks to approximate the estimation of the action-value function. Training of the deep Q-network (DQN) is usually restricted to first order gradient based methods. This paper attempts to accelerate the training of deep Q-networks by introducing a second order Nesterov's accelerated quasi-Newton method. We evaluate the performance of the proposed method on deep reinforcement learning using double DQNs for global routing. The results show that the proposed method can obtain better routing solutions compared to the DQNs trained with first order Adam and RMSprop methods.
