Web: http://arxiv.org/abs/2209.07809

Sept. 19, 2022, 1:11 a.m. | Zhe Zhang, Yukun Zou, Junjie Lai, Qing Xu

cs.LG updates on arXiv.org arxiv.org

Deep Q-learning Network (DQN) is a successful way which combines
reinforcement learning with deep neural networks and leads to a widespread
application of reinforcement learning. One challenging problem when applying
DQN or other reinforcement learning algorithms to real world problem is data
collection. Therefore, how to improve data efficiency is one of the most
important problems in the research of reinforcement learning. In this paper, we
propose a framework which uses the Max-Mean loss in Deep Q-Network (M$^2$DQN).
Instead of …

arxiv network q-learning

More from arxiv.org / cs.LG updates on arXiv.org

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Research Engineer - VFX, Neural Compositing

@ Flawless | Los Angeles, California, United States

[Job-TB] Senior Data Engineer

@ CI&T | Brazil

Data Analytics Engineer

@ The Fork | Paris, France