Abstract
In this paper, we introduce two algorithms for estimating the cost function of expert players engaged in optimal performance within linear continuous-time differential games. Initially, we propose a model-based algorithm, followed by its data-driven model-free extension. Both methods rely on optimal policy gains, obtained by observing Nash equilibrium trajectories of the expert players. The model-free method also utilizes the trajectories of the learner system. This method addresses the limitations found in existing model-free approaches, which may suffer from either high computational costs, limited applicability to specific systems, or both. The effectiveness of the proposed methods is demonstrated through numerical simulations.
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