The authors derive offline and online inverse differential game algorithms that recover all cost-function parameter sets consistent with observed Nash equilibrium trajectories, with convergence guarantees for the online version.
Inverse reinforcement learning for multi-player noncooperative apprentice games,
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Offline and Online Nonlinear Inverse Differential Games with Known and Approximated Cost and Value Function Structures
The authors derive offline and online inverse differential game algorithms that recover all cost-function parameter sets consistent with observed Nash equilibrium trajectories, with convergence guarantees for the online version.