Robust Control of Scientific Simulations with Deep Reinforcement Learning

Executive Summary

We propose advancing state-of-the-art deep reinforcement learning to increase its applicability to real-world problems by improving performance in unseen environments, adding interpretability and  diverse candidate solutions. If successful, our findings will advance breakthroughs in large-scale scientific simulations, precision medicine, and rapid response to novel pathogens, among other discoveries that rely on high-performance computer simulations.

Publications, Presentations, and Patents

Landajuela, Mikel, Brenden K. Petersen, Sookyung Kim, Claudio P. Santiago, Ruben Glatt, Nathan Mundhenk, Jacob F. Pettit, and Daniel Faissol. "Discovering symbolic policies with deep reinforcement learning." International Conference on Machine Learning, pp. 5979-5989. PMLR, 2021.