#hjb-equations News & Analysis

Stabilized neural Hamilton--Jacobi--Bellman solvers: Error analysis and applications in model-based reinforcement learning

Researchers develop a hybrid neural network approach for solving Hamilton-Jacobi-Bellman equations in continuous-time reinforcement learning, combining physics-informed neural solvers with stabilized finite-difference methods. The work provides rigorous error analysis separating residual, policy, and model-identification errors, with experimental validation across multiple control benchmarks.