  In this work, we study policy poisoning through state manipulation, also
known as sensor spoofing, and focus specifically on the case of an agent
forming a control policy through batch learning in a linear-quadratic (LQ)
system. In this scenario, an attacker aims to trick the learner into
implementing a targeted malicious policy by manipulating the batch data before
the agent begins its learning process. An attack model is crafted to carry out
the poisoning strategically, with the goal of modifying the batch data as
little as possible to avoid detection by the learner. We establish an
optimization framework to guide the design of such policy poisoning attacks.
The presence of bi-linear constraints in the optimization problem requires the
design of a computationally efficient algorithm to obtain a solution.
Therefore, we develop an iterative scheme based on the Alternating Direction
Method of Multipliers (ADMM) which is able to return solutions that are
approximately optimal. Several case studies are used to demonstrate the
effectiveness of the algorithm in carrying out the sensor-based attack on the
batch-learning agent in LQ control systems.
