首个可直接运行的流控强化学习基准，支持3D与多智能体。
Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
- 基于PyTorch构建，全可微分，无需外部CFD软件
- 提供PPO、SAC等算法在多种流控场景的基线结果
- 适合做流体力学控制算法对比研究的研究者
强化学习在主动流控（AFC）中展现出潜力，但现有研究因观测与执行方式、数值设置及评估协议不统一而难以比较。现有基准多依赖外部计算流体动力学（CFD）求解器，不可微分，且缺乏3D和多智能体支持。为此，我们提出FluidGym，首个独立、全可微分的强化学习流控基准套件。它基于GPU加速的PICT求解器，在PyTorch中实现，单个Python栈运行，无需外部CFD软件，提供标准化评估协议。我们报告了PPO、SAC、DPC和TD-MPC的基线结果，并公开所有环境、数据集与训练模型。FluidGym支持方法系统性比较，为基于学习的流控研究奠定可扩展基础，项目地址：github.com/safe-autonomous-systems/fluidgym。
原文摘要 · Abstract (English)
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, SAC, DPC, and TD-MPC, 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 github.com/safe-autonomous-systems/fluidgym.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。