Namkyeong Cho

Physics-Informed Approach for Exploratory Hamilton–Jacobi–Bellman Equations via Policy Iterations

We propose a mesh-free policy iteration framework based on physics-informed neural networks (PINNs) for solving entropy-regularized stochastic control problems. The method iteratively alternates between soft policy evaluation and improvement using automatic differentiation and neural approximation,