Reinforcement Learning
stable-baselines3
deep-reinforcement-learning
fluidgym
active-flow-control
fluid-dynamics
simulation
RBC2D-easy-v0
Eval Results (legacy)
Instructions to use safe-autonomous-systems/ppo-RBC2D-easy-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use safe-autonomous-systems/ppo-RBC2D-easy-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="safe-autonomous-systems/ppo-RBC2D-easy-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Add files using upload-large-folder tool
Browse files
README.md
CHANGED
|
@@ -52,3 +52,9 @@ Each seed is contained in its own subdirectory. You can load a model using:
|
|
| 52 |
```python
|
| 53 |
from stable_baselines3 import PPO
|
| 54 |
model = PPO.load("0/ckpt_latest.zip")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 52 |
```python
|
| 53 |
from stable_baselines3 import PPO
|
| 54 |
model = PPO.load("0/ckpt_latest.zip")
|
| 55 |
+
```
|
| 56 |
+
|
| 57 |
+
## References
|
| 58 |
+
|
| 59 |
+
* [Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control](http://arxiv.org/abs/2601.15015)
|
| 60 |
+
* [FluidGym GitHub Repository](https://github.com/safe-autonomous-systems/fluidgym)
|