对抗攻击下LQG控制的博弈分析
A Game Theoretic Analysis of LQG Control under Adversarial Attack
摘要：
受深度强化学习对抗性攻击研究的启发，本文研究了对线性二次高斯控制的欺骗攻击。在所考虑的攻击模型中，对手可以操纵观察的代理受互信息约束。对抗性问题被表述为一种新的动态廉价谈话游戏，以捕捉对手和代理之间的战略互动，信息可用性的不对称性和系统动态。给出了纯策略和行为策略中存在子博弈完美均衡的充要条件，并给出了均衡的特征和控制报酬.结果表明，纯策略均衡是信息均衡，而行为策略均衡只存在咿呀学语均衡。数值结果表明，战略对抗互动的影响。
英文摘要：
Motivated by recent works addressing adversarial attacks on deep reinforcement learning, a deception attack on linear quadratic Gaussian control is studied in this paper. In the considered attack model, the adversary can manipulate the observation of the agent subject to a mutual information constraint. The adversarial problem is formulated as a novel dynamic cheap talk game to capture the strategic interaction between the adversary and the agent, the asymmetry of information availability, and the system dynamics. Necessary and sufficient conditions are provided for subgame perfect equilibria to exist in pure strategies and in behavioral strategies; and characteristics of the equilibria and the resulting control rewards are given. The results show that pure strategy equilibria are informative, while only babbling equilibria exist in behavioral strategies. Numerical results are shown to illustrate the impact of strategic adversarial interaction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
