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policy iteration

topic1 events
papersSEP 11 04:00 UTC

Monotone Neural Policy Iteration Method for High-Dimensional HJB Equations

The paper studies a neural semi-discrete scheme for solving high-dimensional first-order Hamilton-Jacobi-Bellman equations, assuming either known or learned dynamics. Monotonicity is achieved by combining centered differences with an artificial viscosity term that scales linearly with the mesh size. The authors evaluate the resulting monotone operator within a policy iteration framework.

research/optimal-control/numerical-methods/neural-networks