Qualitative Measurements of Policy Discrepancy for Return-Based Deep Q-Network

no code implementations • 14 Jun 2018 • Wenjia Meng, Qian Zheng, Long Yang, Pengfei Li, Gang Pan

In this paper, we propose a general framework to combine DQN and most of the return-based reinforcement learning algorithms, named R-DQN.

OpenAI Gym reinforcement-learning +1

A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning

no code implementations • 9 Feb 2018 • Long Yang, Minhao Shi, Qian Zheng, Wenjia Meng, Gang Pan

Results show that, with an intermediate value of $\sigma$, $Q(\sigma ,\lambda)$ creates a mixture of the existing algorithms that can learn the optimal value significantly faster than the extreme end ($\sigma=0$, or $1$).

Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices

no code implementations • 2 Jan 2017 • Wenjia Meng, Zonghua Gu, Ming Zhang, Zhaohui Wu

With the rapid proliferation of Internet of Things and intelligent edge devices, there is an increasing need for implementing machine learning algorithms, including deep learning, on resource-constrained mobile embedded devices with limited memory and computation power.

Computational Efficiency General Classification +2