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

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