<ul><li>Researchers propose the Peer-Aware Cost Estimation (PACE) framework for learning the cost parameters of another agent in a linear quadratic differential game with incomplete information.</li><li>PACE treats the other agent as a learning agent rather than a stationary optimal agent and models their learning dynamics to infer their cost function parameters.</li><li>The PACE framework enables agents to adapt their control policies based on real-time inference of each other's objective functions, using only previous state observations.</li><li>Numerical studies show that modeling the learning dynamics of the other agent improves stability and convergence speed compared to approaches assuming complete information.</li></ul>

PACE: A Framework for Learning and Control in Linear Incomplete-Information Differential Games

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