تقرير
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training
| العنوان: | Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training |
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| المؤلفون: | Sharma, Piyush K., Fernandez, Rolando, Zaroukian, Erin, Dorothy, Michael, Basak, Anjon, Asher, Derrik E. |
| المصدر: | Published at: Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117462K (2021), SPIE Defense + Commercial Sensing, 2021, Online Only |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Multiagent Systems, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks. Comment: This article appeared in the news at: https://www.army.mil/article/247261/army_researchers_develop_innovative_framework_for_training_ai |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1117/12.2585808 |
| URL الوصول: | http://arxiv.org/abs/2107.14316 |
| رقم الأكسشن: | edsarx.2107.14316 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1117/12.2585808 |
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