A New Efficient Method for Refining the Reinforcement Learning Algorithm to Train Deep Q Agents for Reaching a Consensus in P2P Networks-Reference-Cited by-同舟云学术

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A New Efficient Method for Refining the Reinforcement Learning Algorithm to Train Deep Q Agents for Reaching a Consensus in P2P Networks

Published:2023 Issue: Volume:11 Page:38665-38679
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mallouh Arafat Abu¹^ORCID,Qawaqneh Zakariya²,Abuzaghleh Omar³,Al-Rababa’A Ahmad⁴^ORCID

Affiliation:

1. Computer Science Department, Manhattan College, Riverdale, NY, USA

2. Department of Computing Sciences, State University of New York Brockport, Brockport, NY, USA

3. Department of Computer Information Science, Higher Colleges of Technology, Dubai, United Arab Emirates

4. Computer Science Department, The World Islamic Science and Education University, Amman, Jordan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10105270.pdf?arnumber=10105270

Reference34 articles.

1. Technical report on the CleverHans v2. 1.0 adversarial examples library;papernot;arXiv 1610 00768,2016

2. Practical Byzantine fault tolerance;castro;Proc OSDI,1999

3. A multiagent deep deterministic policy gradient-based distributed protection method for distribution network

4. A deep Q-learning portfolio management framework for the cryptocurrency market

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