Monotone Neural Policy Iteration for High-Dimensional First-Order Hamilton--Jacobi--Bellman Equations
Solving high-dimensional first-order Hamilton-Jacobi-Bellman equations is achieved by framing them as optimal control tasks. By incorporating monotonicity constraints into neural networks, the method improves stability, convergence, and predictability. This approach blends mathematical rigor with deep learning to enable reliable control in complex, safety-critical systems.