FluidGym Benchmark Models
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Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
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PPO on RBC2D-hard-v0 (FluidGym)
This repository is part of the FluidGym benchmark results. It contains trained Stable Baselines3 agents for the specialized RBC2D-hard-v0 environment.
Evaluation Results
Global Performance (Aggregated across 5 seeds)
Mean Reward: -0.31 ± 0.03
Per-Seed Statistics
| Run | Mean Reward | Std Dev |
|---|---|---|
| Seed 0 | -0.32 | 2.24 |
| Seed 1 | -0.27 | 2.19 |
| Seed 2 | -0.36 | 2.50 |
| Seed 3 | -0.29 | 2.22 |
| Seed 4 | -0.30 | 2.35 |
About FluidGym
FluidGym is a benchmark for reinforcement learning in active flow control.
Usage
Each seed is contained in its own subdirectory. You can load a model using:
from stable_baselines3 import PPO
model = PPO.load("0/ckpt_latest.zip")
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Collection including safe-autonomous-systems/ppo-RBC2D-hard-v0
Evaluation results
- mean_reward on FluidGym-RBC2D-hard-v0self-reported-0.310