  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.
