Deep Q-Networks for Accelerating the Training of Deep Neural Networks

Understand

In this paper, we propose a principled deep reinforcement learning (RL) approach that is able to accelerate the convergence rate of general deep neural networks (DNNs).

• With our approach, a deep RL agent (synonym for optimizer in this work) is used to automatically learn policies about how to schedule learning rates during the optimization of a DNN.
• The state features of the agent are learned from the weight statistics of the optimizee during training.
• The reward function of this agent is designed to learn policies that minimize the optimizee's training time given a certain performance goal.