Multi-Agent Partial Observable Safe Reinforcement Learning for Counter Uncrewed Aerial Systems-Reference-Cited by-同舟云学术

Multi-Agent Partial Observable Safe Reinforcement Learning for Counter Uncrewed Aerial Systems

Author:

Pierre Jean-Elie¹^ORCID,Sun Xiang¹^ORCID,Fierro Rafael¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, USA

Funder

National Science Foundation

Air Force Research Laboratory

Sandia National Laboratories

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

Reference38 articles.

1. Safe deep reinforcement learning for multi-agent systems with continuous action spaces;sheebaelhamd;arXiv 2108 03952,2021

2. The before, during, and after of multi-robot deadlock;grover;Int J Robot Res,2016

3. Safe exploration in continuous action spaces;dalal;arXiv 1801 08757,2018

4. High-dimensional continuous control using generalized advantage estimation;schulman;arXiv 1506 02438 [cs],2015

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