Web: http://arxiv.org/abs/2209.07089

Sept. 16, 2022, 1:11 a.m. | Long Yang, Jiaming Ji, Juntao Dai, Linrui Zhang, Binbin Zhou, Pengfei Li, Yaodong Yang, Gang Pan

cs.LG updates on arXiv.org arxiv.org

Safe reinforcement learning (RL) studies problems where an intelligent agent
has to not only maximize reward but also avoid exploring unsafe areas. In this
study, we propose CUP, a novel policy optimization method based on Constrained
Update Projection framework that enjoys rigorous safety guarantee. Central to
our CUP development is the newly proposed surrogate functions along with the
performance bound. Compared to previous safe RL methods, CUP enjoys the
benefits of 1) CUP generalizes the surrogate functions to generalized advantage …

arxiv optimization policy projection

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