Jacobian-Free Backpropagation shows convergence for implicit Hamiltonian optimal control

Researchers have established convergence guarantees for Jacobian-Free Backpropagation (JFB) in stochastic minibatch settings for optimal control problems with implicit Hamiltonians. This theoretical advancement demonstrates that JFB updates can converge to stationary points of the expected optimal control objective. The study also empirically validates JFB's scalability on complex, high-dimensional problems, including multi-agent consumption and quadrotor control. AI

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IMPACT Provides theoretical justification and empirical evidence for using Jacobian-Free Backpropagation in high-dimensional optimal control.

RANK_REASON Academic paper introducing theoretical convergence guarantees for a machine learning technique applied to optimal control problems.

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• Eric Gelphman
• arXiv
• Jacobian-Free Backpropagation

• paper
• other