Suboptimality of Constrained Action Adversarial Cyber-Physical Games

Takuma A. Adams (), Andrew C. Cullen () and Tansu Alpcan ()
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Takuma A. Adams: The University of Melbourne
Andrew C. Cullen: The University of Melbourne
Tansu Alpcan: The University of Melbourne

Dynamic Games and Applications, 2025, vol. 15, issue 3, No 3, 769-788

Abstract: Abstract Analysing complex cyber-physical systems using established game-theoretic tools poses significant challenges due to the nonlinear dynamics inherent to such systems. To address this, we leverage multi-agent reinforcement learning (MARL) to study the impact constrained action spaces have on a player’s ability to uncover optimal strategies in a system governed by adversarial nonlinear dynamics. The system is posed as a dynamic, two-player, zero-sum game with elements of adversarial decision-making and resource competition, making it suitable for a variety of cyber-security, business, and military scenarios. Comparing player strategies over an ensemble of different action spaces suggests that MARL converges to an approximate $$\epsilon $$ ϵ -Nash equilibrium even under constraints. In addition, numerical results reveal agreement between MARL solutions and our theoretical understanding of the problem, offering insight into action space selection for this adversarial game.

Keywords: Cyber-physical systems; Adversarial games; Multi-agent reinforcement learning; Dynamical systems (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13235-025-00631-9

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