Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality

• URL: http://arxiv.org/abs/2106.12928v1
• Date: Thu, 24 Jun 2021 11:43:38 GMT
• Title: Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality
• Authors: Stefanos Leonardos, Georgios Piliouras, Kelly Spendlove,
• Abstract summary: We study smooth Q-learning, a prototypical learning model that 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.
• Score: 21.94743452608215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, we show that fast convergence of Q-learning in competitive settings is obtained 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.

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