Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR

Shahbaz P Qadri Syed; He Bai

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arXiv · 2504.20927

Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR

Shahbaz P Qadri Syed·He Bai

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Abstract

Developing scalable and efficient reinforcement learning algorithms for cooperative multi-agent control has received significant attention over the past years. Existing literature has proposed inexact decompositions of local Q-functions based on empirical information structures between the agents. In this paper, we exploit inter-agent coupling information and propose a systematic approach to exactly decompose the local Q-function of each agent. We develop an approximate least square policy iteration algorithm based on the proposed decomposition and identify two architectures to learn the local Q-function for each agent. We establish that the worst-case sample complexity of the decomposition is equal to the centralized case and derive necessary and sufficient graphical conditions on the inter-agent couplings to achieve better sample efficiency. We demonstrate the improved sample efficiency and computational efficiency on numerical examples.

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BibTeX RIS

Shahbaz P Qadri Syed, He Bai. 2025-04-29. Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR. https://arxiv.org/abs/2504.20927

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