Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
Reinforcement learning (RL) has shown promising results in active flow control (AFC), yet progress in the field remains difficult to assess as existing studies rely on heterogeneous observation and actuation schemes, numerical setups, and evaluation protocols. Current AFC benchmarks attempt to address these issues but heavily rely on external computational fluid dynamics (CFD) solvers, are not fully differentiable, and provide limited 3D and multi-agent support. To overcome these limitations, we introduce FluidGym, the first standalone, fully differentiable benchmark suite for RL in AFC. Built entirely in PyTorch on top of the GPU-accelerated PICT solver, FluidGym runs in a single Python stack, requires no external CFD software, and provides standardized evaluation protocols. We present baseline results with PPO and SAC and release all environments, datasets, and trained models as public resources. FluidGym enables systematic comparison of control methods, establishes a scalable foundation for future research in learning-based flow control, and is available at https://github.com/safe-autonomous-systems/fluidgym.
69777ba64c988cc0118d1d99
Jannis Becktepe
claimed_verified
2026-01-27T09:43:19.480Z
682b01713e2301e26aff86ca
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/cDNT2fo7ujfmzabB2AKzW.png
Jannis Becktepe
becktepe
user
becktepe
69777ba64c988cc0118d1d9a
Aleksandra Franz
69777ba64c988cc0118d1d9b
Nils Thuerey
admin_assigned
2026-01-27T14:39:16.341Z
667ab4c3b3c1b9aac81388cc
https://cdn-avatars.huggingface.co/v1/production/uploads/667ab4c3b3c1b9aac81388cc/G9uE6FaR8BZf1yoBvaApY.jpeg
Nils Thuerey
thunil
user
thunil
69777ba64c988cc0118d1d9c
Sebastian Peitz
claimed_verified
2026-02-04T12:34:15.704Z
69833bc1db1504b093a036fa
https://cdn-avatars.huggingface.co/v1/production/uploads/69833bc1db1504b093a036fa/f52jOZBFSaUbKZvau1mHm.jpeg
Sebastian Peitz
SebastianPeitz
user
SebastianPeitz
Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
Reinforcement learning (RL) has shown promising results in active flow control (AFC), yet progress in the field remains difficult to assess as existing studies rely on heterogeneous observation and actuation schemes, numerical setups, and evaluation protocols. Current AFC benchmarks attempt to address these issues but heavily rely on external computational fluid dynamics (CFD) solvers, are not fully differentiable, and provide limited 3D and multi-agent support. To overcome these limitations, we introduce FluidGym, the first standalone, fully differentiable benchmark suite for RL in AFC. Built entirely in PyTorch on top of the GPU-accelerated PICT solver, FluidGym runs in a single Python stack, requires no external CFD software, and provides standardized evaluation protocols. We present baseline results with PPO and SAC and release all environments, datasets, and trained models as public resources. FluidGym enables systematic comparison of control methods, establishes a scalable foundation for future research in learning-based flow control, and is available at https://github.com/safe-autonomous-systems/fluidgym.
FluidGym presents a standalone, fully differentiable reinforcement learning benchmark for active flow control that operates without external CFD solvers and supports standardized evaluation protocols.
reinforcement learning active flow control PyTorch PICT solver differentiable benchmark PPO SAC