A convex data-driven inverse RL framework for linear systems with uncertainty that uses a generalized LQR cost with cross terms, kernel regression from data, and differentiable SDPs for robust cost design over perturbations.
IEEE Transactions on Cybernetics , year=
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Data-Driven Inverse Reinforcement Learning of Linear Systems with Model Uncertainty: A Convex Optimization View
A convex data-driven inverse RL framework for linear systems with uncertainty that uses a generalized LQR cost with cross terms, kernel regression from data, and differentiable SDPs for robust cost design over perturbations.