Survey of recent multi-agent reinforcement learning algorithms utilizing centralized training

Piyush K. Sharma; Rolando Fernandez; Erin Zaroukian; Michael Dorothy; Anjon Basak; Derrik E. Asher

doi:10.1117/12.2585808

12 April 2021 Survey of recent multi-agent reinforcement learning algorithms utilizing centralized training

Piyush K. Sharma, Rolando Fernandez, Erin Zaroukian, Michael Dorothy, Anjon Basak, Derrik E. Asher

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117462K (2021) https://doi.org/10.1117/12.2585808
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution approach to improve human-like collaboration in cooperative tasks. Here, we introduce variations of centralized training to describe cases where shared/independent reward structure is utilized to improve learning by training agents in an intelligent way, and to analyze cooperative behavior in multi-agent systems. This work discusses implications for providing a classification of the recent MARL algorithmic approaches on the basis of their information sharing mechanism (e.g., reward, gradient, action, parameter, observation/state space sharing) on cooperative behavior in multi-agent systems.

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Citation Download Citation

Piyush K. Sharma, Rolando Fernandez, Erin Zaroukian, Michael Dorothy, Anjon Basak, and Derrik E. Asher "Survey of recent multi-agent reinforcement learning algorithms utilizing centralized training", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117462K (12 April 2021); https://doi.org/10.1117/12.2585808

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