For maximum-likelihood IRL, the inner-problem Hessian at a realizable optimum equals the temperature-scaled trajectory Fisher matrix, which enables a scalable sketched hypergradient method.
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Efficient Hypergradient Descent for Inverse Reinforcement Learning
For maximum-likelihood IRL, the inner-problem Hessian at a realizable optimum equals the temperature-scaled trajectory Fisher matrix, which enables a scalable sketched hypergradient method.