In diesem Papier, we address the problem of a two-player linear quadratic
differential game with incomplete information, a scenario commonly encountered
in multi-agent control, human-robot interaction (HRI), and approximation
methods for solving general-sum differential games. While solutions to such
linear differential games are typically obtained through coupled Riccati
equations, the complexity increases when agents have incomplete information,
particularly when neither is aware of the other’s cost function. To tackle this
challenge, we propose a model-based Peer-Aware Cost Estimation (PACE) framework
for learning the cost parameters of the other agent. In PACE, each agent treats
its peer as a learning agent rather than a stationary optimal agent, models
their learning dynamics, and leverages this dynamic to infer the cost function
parameters of the other agent. This approach enables agents to infer each
other’s objective function in real time based solely on their previous state
observations and dynamically adapt their control policies. Furthermore, we
provide a theoretical guarantee for the convergence of parameter estimation and
the stability of system states in PACE. Additionally, in our numerical studies,
we demonstrate how modeling the learning dynamics of the other agent benefits
PACE, compared to approaches that approximate the other agent as having
complete information, particularly in terms of stability and convergence speed.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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