Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training

• URL: http://arxiv.org/abs/2107.14316v1
• Date: Thu, 29 Jul 2021 20:29:12 GMT
• Title: Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training
• Authors: Piyush K. Sharma, Rolando Fernandez, Erin Zaroukian, Michael Dorothy, Anjon Basak, and Derrik E. Asher
• Abstract summary: 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.
• Score: 0.7588690078299698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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