Multiagent learning is not the answer . It is the question

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Multiagent Learning: Basics, Challenges, and Prospects (2012, background): “Critical reflections of this classification can be for instance found in Stone (2007) and Tuyls and Parsons (2007).”
A survey and critique of multiagent deep reinforcement learning (2018, neutral): “The title of this work makes a clear reference to previous seminal MAL works [2, 354].”
Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games (2012, background): “Recently, an active research topic is to investigate the asymptotic behaviors by incorporating RL technologies with game theory [46]–[48].”
Multiagent Learning of Coordination in Loosely Coupled Multiagent Systems (2015, methodology): “MAL uses techniques and concepts from disciplines such as artificial intelligence, game theory, psychology, cogni tio and sociology, and has attracted a great deal of interest in the r es arch community in recent years [10], [11], [12], [13], [14].”
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control (2009, background): “3, SG is not the only approach available [75].”