On the Stability of Learning in Network Games with Many Players

Multi-agent learning algorithms have been shown to display complex, unstable behaviours in a wide array of games. In fact, previous works indicate that convergent behaviours are less likely to occur as the total number of agents increases. This seemingly prohibits convergence to stable strategies, s...

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Veröffentlicht in:	arXiv.org 2024-03
Hauptverfasser:	Hussain, Aamal, Leonte, Dan, Belardinelli, Francesco, Piliouras, Georgios
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Convergence Equilibrium Game theory Games Machine learning Multiagent systems Players
Online-Zugang:	Volltext
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