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Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality
Stefanos Leonardos · Kelly Spendlove · Georgios Piliouras

The interplay between exploration and exploitation in competitive multi-agent learning is still far from being well understood. Motivated by this, we study smooth Q-learning, a prototypical learning model that explicitly captures the balance between game rewards and exploration costs. We show that Q-learning always converges to the unique quantal-response equilibrium (QRE), the standard solution concept for games under bounded rationality, in weighted zero-sum polymatrix games with heterogeneous learning agents using positive exploration rates. Complementing recent results about convergence in weighted potential games [15,34], we show that fast convergence of Q-learning in competitive settings obtains regardless of the number of agents and without any need for parameter fine-tuning. As showcased by our experiments in network zero-sum games, these theoretical results provide the necessary guarantees for an algorithmic approach to the currently open problem of equilibrium selection in competitive multi-agent settings.

Author Information

Stefanos Leonardos (Singapore University of Technology and Design)
Kelly Spendlove (University of Oxford)
Georgios Piliouras (Singapore University of Technology and Design)

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