CuMARL: Curiosity-Based Learning in Multiagent Reinforcement Learning-Reference-Cited by-同舟云学术

CuMARL: Curiosity-Based Learning in Multiagent Reinforcement Learning

Author:

Ningombam Devarani Devi¹^ORCID,Yoo Byunghyun²^ORCID,Kim Hyun Woo²,Song Hwa Jeon²^ORCID,Yi Sungwon²^ORCID

Affiliation:

1. Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India

2. Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea

Funder

Electronics and Telecommunications Research Institute (ETRI) funded by the Korean Government

Core Technology Research for Self-Improving Integrated Artificial Intelligence System

Exploratory Research for Human-Agent Collaboration in Multi-Agent Games

AFOSR

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference29 articles.

1. Collaborative evolutionary reinforcement learning;khadka;Proc 36th Int Conf Mach Learn (PMLR),2019

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