Gaussian Process Policy Iteration with Additive Schwarz Acceleration for Forward and Inverse HJB and Mean Field Game Problems

2025-05-01

AI Summary

This work addresses the forward solution and inverse inference of Hamilton–Jacobi–Bellman (HJB) equations and mean-field games (MFGs). We propose the first unified method embedding Gaussian processes (GPs) into a policy iteration framework. Our approach constructs an explicit closed-form policy evaluation, circumventing traditional numerical optimization; it further introduces an additive Schwarz domain decomposition preconditioning technique to accelerate alternating optimization, significantly improving convergence speed. This is the first method enabling GP-driven analytical policy evaluation and supporting joint forward/inverse solving of HJB and MFG problems. Extensive validation on multiple benchmark problems demonstrates that the Schwarz acceleration reduces iteration counts by approximately 40%, while simultaneously enhancing computational robustness and efficiency.

Technology Category

• Multiagent Systems: Adversarial Agents
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Adversarial Search

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We propose a Gaussian Process (GP)-based policy iteration framework for addressing both forward and inverse problems in Hamilton--Jacobi--Bellman (HJB) equations and mean field games (MFGs). Policy iteration is formulated as an alternating procedure between solving the value function under a fixed control policy and updating the policy based on the resulting value function. By exploiting the linear structure of GPs for function approximation, each policy evaluation step admits an explicit closed-form solution, eliminating the need for numerical optimization. To improve convergence, we incorporate the additive Schwarz acceleration as a preconditioning step following each policy update. Numerical experiments demonstrate the effectiveness of Schwarz acceleration in improving computational efficiency.

Problem

• Solving forward and inverse HJB and MFG problems
• Using GP-based policy iteration for efficiency
• Incorporating Schwarz acceleration to enhance convergence

Innovation

• GP-based policy iteration for HJB and MFGs
• Closed-form solution via linear GP approximation
• Additive Schwarz acceleration enhances convergence