Version 1
: Received: 6 April 2024 / Approved: 11 April 2024 / Online: 17 April 2024 (03:29:46 CEST)
How to cite:
Beikmohammadi, A. Learning to Communicate through Multi-Agent Reinforcement Learning (MARL): A Systematic Literature Review. Preprints2024, 2024040813. https://doi.org/10.20944/preprints202404.0813.v1
Beikmohammadi, A. Learning to Communicate through Multi-Agent Reinforcement Learning (MARL): A Systematic Literature Review. Preprints 2024, 2024040813. https://doi.org/10.20944/preprints202404.0813.v1
Beikmohammadi, A. Learning to Communicate through Multi-Agent Reinforcement Learning (MARL): A Systematic Literature Review. Preprints2024, 2024040813. https://doi.org/10.20944/preprints202404.0813.v1
APA Style
Beikmohammadi, A. (2024). Learning to Communicate through Multi-Agent Reinforcement Learning (MARL): A Systematic Literature Review. Preprints. https://doi.org/10.20944/preprints202404.0813.v1
Chicago/Turabian Style
Beikmohammadi, A. 2024 "Learning to Communicate through Multi-Agent Reinforcement Learning (MARL): A Systematic Literature Review" Preprints. https://doi.org/10.20944/preprints202404.0813.v1
Abstract
Recent years have witnessed significant advances in reinforcement learning (RL), which has registered remarkable success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Although, typically, the communication protocol between agents is manually specified and not altered during training, recently, some papers have shown signs of trying to emerge a communication between agents on the one hand and, on the other hand, to understand what is exchanged between agents. So, there is a growing body of literature on this topic which includes qualitative and quantitative studies and the ones that apply mixed methods. This study presents the scoping review of the methodological strategies undertaken in a total of 16 research articles. The results present the critical appraisal of quantitative methods in terms of validity and reliability and for qualitative methods considering four trustworthiness factors. In the end, relevant insights are further explored with implications and reflections on how they can benefit one's research in the field.
Keywords
Reinforcement Learning; POMDP; Learn to Communicate; Systematic Literature Review; Quantitative Methods; Qualitative Methods
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
