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

• URL: http://arxiv.org/abs/2010.10901v2
• Date: Fri, 22 Jan 2021 22:18:21 GMT
• Title: On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality
• Authors: Ezra Tampubolon, Haris Ceribasic, Holger Boche
• Abstract summary: We study the system of interacting non-cooperative two Q-learning agents. We show that this information asymmetry can lead to a stable outcome of population learning.
• Score: 78.76529463321374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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