Relexi — A scalable open source reinforcement learning framework for high-performance computing
Marius Kurz, Philipp Offenhäuser, Dominic Viola, Michael Resch, Andrea Beck
Abstract
Relexi is an open source reinforcement learning (RL) framework written in Python and based on TensorFlow’s RL library TF-Agents. Relexi allows to employ RL for environments that require computationally intensive simulations like applications in computational fluid dynamics. For this, Relexi couples legacy simulation codes with the RL library TF-Agents at scale on modern high-performance computing (HPC) hardware using the SmartSim library. Relexi thus provides an easy way to explore the potential of RL for HPC applications.
Topics & Concepts
Reinforcement learningComputer scienceScalabilityOpen sourceReinforcementDistributed computingComputer architectureHuman–computer interactionArtificial intelligenceOperating systemEngineeringSoftwareStructural engineeringReinforcement Learning in RoboticsEvolutionary Algorithms and ApplicationsModel Reduction and Neural Networks