Distributed deep reinforcement learning for simulation control

TL;DR

This paper introduces a deep reinforcement learning framework to dynamically control hyperparameters in scientific simulations, improving efficiency and convergence in chaotic systems and CFD solvers.

Contribution

It presents a novel RL-based approach for adaptive hyperparameter tuning during simulation runtime, reducing manual effort and computational costs.

Findings

RL control reduces iterations for CFD convergence.

RL effectively manages chaos in dynamical systems.

Framework adaptable to different simulation geometries.

Abstract

Several applications in the scientific simulation of physical systems can be formulated as control/optimization problems. The computational models for such systems generally contain hyperparameters, which control solution fidelity and computational expense. The tuning of these parameters is non-trivial and the general approach is to manually `spot-check' for good combinations. This is because optimal hyperparameter configuration search becomes impractical when the parameter space is large and when they may vary dynamically. To address this issue, we present a framework based on deep reinforcement learning (RL) to train a deep neural network agent that controls a model solve by varying parameters dynamically. First, we validate our RL framework for the problem of controlling chaos in chaotic systems by dynamically changing the parameters of the system. Subsequently, we illustrate the capabilities of our framework for accelerating the convergence of a steady-state CFD solver by automatically adjusting the relaxation factors of discretized Navier-Stokes equations during run-time. The results indicate that the run-time control of the relaxation factors by the learned policy leads to a significant reduction in the number of iterations for convergence compared to the random selection of the relaxation factors. Our results point to potential benefits for learning adaptive hyperparameter learning strategies across different geometries and boundary conditions with implications for reduced computational campaign expenses. \footnote{Data and codes available at \url{https://github.com/Romit-Maulik/PAR-RL}}