DIP-QL: A Novel Reinforcement Learning Method for Constrained Industrial Systems-Reference-Cited by-同舟云学术

DIP-QL: A Novel Reinforcement Learning Method for Constrained Industrial Systems

Author:

Park Hyungjun¹,Min Daiki²,Ryu Jong-hyun³,Choi Dong Gu¹^ORCID

Affiliation:

1. Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea

2. School of Business, Ewha Womans University, Seoul, South Korea

3. College of Business Management, Hongik University, Sejong, South Korea

Funder

National Research Foundation of Korea

Korea Institute of Energy Technology Evaluation and Planning

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

Reference29 articles.

1. Constrained policy optimization;achiam;Proc 34th Int Conf Mach Learn,0

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