FluidGym Benchmark Models
Collection
Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
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66 items
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Updated
This repository is part of the FluidGym benchmark results. It contains trained Stable Baselines3 agents for the specialized RBC2D-medium-v0 environment.
Mean Reward: 0.06 ± 0.15
| Run | Mean Reward | Std Dev |
|---|---|---|
| Seed 0 | -0.06 | 1.56 |
| Seed 1 | 0.27 | 1.35 |
| Seed 2 | 0.21 | 0.93 |
| Seed 3 | -0.12 | 1.43 |
| Seed 4 | 0.00 | 1.38 |
FluidGym is a benchmark for reinforcement learning in active flow control.
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")