Multiagent Learning of Coordination in Loosely Coupled Multiagent Systems

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Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns (2019, methodology): “…coordination graphs, recent work focuses on automatically identifying interaction areas during the learning process and can be applied to more general learning tasks [De Hauwere et al. , 2010; Hauwere et al. , 2011; Kok et al. , 2005; Melo and Veloso, 2009; Melo and Veloso, 2011; Yu et al. , 2015].”
Multi-Agent Game Abstraction via Graph Attention Neural Network (2019, background): “Earlier work focuses on loosely coupled multi-agent systems, and adopt techniques such as game abstraction and knowledge transfer to help with speeding up multi-agent reinforcement learning(Guestrin, Lagoudakis, and Parr 2002; Kok and Vlassis 2004; De Hauwere, Vrancx, and Nowé 2010; Melo and Veloso ”
Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs (2020, background): “multi-robot systems and resource management to automated trading [49]–[52].”
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity (2017, background): “A different approach is to determine the degree of interaction among agents, this could help in defining whether to interact with an agent or ignore it and take it as part of the environment (De Hauwere et al., 2010; Yu et al., 2015).”