Reproduce “A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers” on OpenResearch

About this paper

David Huergo, Laura Alonso, Saumitra Joshi, Adrian Juanicoteca, Gonzalo Rubio, Esteban Ferrer

Researchers from Universidad Politécnica de Madrid developed a reinforcement learning strategy using Proximal Policy Optimization (PPO) to automate and accelerate h/p-multigrid solvers for high-order partial differential equations. This approach achieved over 100-fold speedups in simulation run-times and demonstrated improved robustness across varied problem parameters compared to traditional manual tuning.

Key insights

• Reinforcement learning, specifically PPO, can effectively automate and optimize the complex parameter tuning of h/p-multigrid methods in high-order PDE solvers, reducing the need for manual expertise.
• Training the RL agent in more complex and representative environments (e.g., non-uniform meshes and full h/p-multigrid) is crucial for achieving better generalization capabilities to varying problem setups.
• The PPO-driven approach significantly enhances solver robustness by providing consistent and stable performance across a wide range of equation coefficients and mesh types, unlike heuristically tuned methods.

Summary by alphaXiv.