An Augmented Game Approach for Design and Analysis of Distributed Learning Dynamics in Multiagent Games

Area of Science:

• Game Theory
• Multiagent Systems
• Distributed Learning

Background:

• Analyzing distributed learning dynamics in multiagent games is complex due to coupled utility functions.
• Existing methods struggle to systematically formulate and assess the convergence of these dynamics.

Purpose of the Study:

• To propose an augmented game approach for formulating and analyzing distributed learning dynamics in multiagent games.
• To investigate the convergence properties of distributed gradient play under this new framework.

Main Methods:

• Reformulating the coupling structure of utility functions into an arbitrary undirected connected network using an augmented game.
• Recasting full-information game learning dynamics into a distributed form.
• Applying the approach to deterministic and stochastic distributed gradient play.

Main Results:

• The augmented game preserves Nash equilibria but may alter convergence properties.
• Demonstrated cases where classic gradient play converges but distributed play does not, and vice versa.
• Showcased that variational stability structures are not always preserved in the augmented game.

Conclusions:

• The augmented game approach provides a systematic methodology for formulating and analyzing distributed game learning dynamics.
• Convergence of distributed learning is not guaranteed even when Nash equilibria are preserved.
• The framework offers insights into the feasibility and limitations of distributed learning in multiagent systems.