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arXiv · 2010.10901

On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality

Ezra Tampubolon·Haris Ceribasic·Holger Boche
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Abstract

In this work, we study the system of interacting non-cooperative two Q-learning agents, where one agent has the privilege of observing the other's actions. We show that this information asymmetry can lead to a stable outcome of population learning, which generally does not occur in an environment of general independent learners. The resulting post-learning policies are almost optimal in the underlying game sense, i.e., they form a Nash equilibrium. Furthermore, we propose in this work a Q-learning algorithm, requiring predictive observation of two subsequent opponent's actions, yielding an optimal strategy given that the latter applies a stationary strategy, and discuss the existence of the Nash equilibrium in the underlying information asymmetrical game.

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Ezra Tampubolon, Haris Ceribasic, Holger Boche. 2020-10-21. On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality. https://arxiv.org/abs/2010.10901

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