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Deep Q-learning method is one of the most popularly used deep reinforcementlearning algorithms which uses deep neural networks to approximate theestimation of the action-value function. Training of the deep Q-network (DQN)is usually restricted to first order gradient based methods. This paperattempts to accelerate the training of deep Q-networks by introducing a secondorder Nesterov's accelerated quasi-Newton method. We evaluate the performanceof the proposed method on deep reinforcement learning using double DQNs forglobal routing. The results show that the proposed method can obtain betterrouting solutions compared to the DQNs trained with first order Adam andRMSprop methods.

A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning