Deep Q-networks For Accelerating The Training Of Deep Neural Networks

Abstract

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. The actions of the agent correspond to changing the learning rate for the optimizee during training. As far as we know, this is the first attempt to use deep RL to learn how to optimize a large-sized DNN. We perform extensive experiments on a standard benchmark dataset and demonstrate the effectiveness of the policies learned by our approach.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyfu2016deep

Related papers

• Accelerated Methods For Deep Reinforcement Learning (2018)0.00
• An Integrated Approach To Neural Architecture Search For Deep Q-networks (2025)0.00
• Adaptive \(q\)-network: On-the-fly Target Selection For Deep Reinforcement Learning (2024)0.00
• M\(^2\)DQN: A Robust Method For Accelerating Deep Q-learning Network (2022)0.00
• Efficient Off-policy Reinforcement Learning Via Brain-inspired Computing (2022)8.35
• Dynamic Sparse Training For Deep Reinforcement Learning (2021)0.00
• Quantile-based Deep Reinforcement Learning Using Two-timescale Policy Gradient Algorithms (2023)0.00
• An Adaptive Synchronization Approach For Weights Of Deep Reinforcement Learning (2020)0.00