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This repository is part of the
FluidGym
benchmark results. It contains trained Stable Baselines3 agents for the specialized
TCFSmall3D-both-hard-v0
environment.
Evaluation Results
Global Performance (Aggregated across 5 seeds)
Mean Reward:
0.08 ± 0.07
Per-Seed Statistics
Run
Mean Reward
Std Dev
Seed 0
0.07
0.05
Seed 1
0.13
0.05
Seed 2
-0.03
0.03
Seed 3
0.07
0.04
Seed 4
0.16
0.06
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 SAC
model = SAC.load("0/ckpt_latest.zip")
**Important:** The models were trained using ```fluidgym==0.0.2```. In order to use
them with newer versions of FluidGym, you need to wrap the environment with a
`FlattenObservation` wrapper as shown below:
```python
import fluidgym
from fluidgym.wrappers import FlattenObservation
from stable_baselines3 import SAC
env = fluidgym.make("TCFSmall3D-both-hard-v0")
env = FlattenObservation(env)
model = SAC.load("path_to_model/ckpt_latest.zip")
obs, info = env.reset(seed=42)
action, _ = model.predict(obs, deterministic=True)
obs, reward, terminated, truncated, info = env.step(action)
Runs of safe-autonomous-systems ma-sac-TCFSmall3D-both-hard-v0 on huggingface.co
224
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
21
30-day runs
More Information About ma-sac-TCFSmall3D-both-hard-v0 huggingface.co Model
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