Reinforcement Learning
stable-baselines3
deep-reinforcement-learning
fluidgym
active-flow-control
fluid-dynamics
simulation
RBC2D-hard-v0
Eval Results (legacy)
Instructions to use safe-autonomous-systems/sac-RBC2D-hard-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use safe-autonomous-systems/sac-RBC2D-hard-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="safe-autonomous-systems/sac-RBC2D-hard-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download 2/ckpt_latest.zip from safe-autonomous-systems/sac-RBC2D-hard-v0: direct link, hf CLI and curl.
- Browser
- Download file 16.1 MB
-
https://huggingface.co/safe-autonomous-systems/sac-RBC2D-hard-v0/resolve/main/2/ckpt_latest.zip
- Command line
-
hf download hf://safe-autonomous-systems/sac-RBC2D-hard-v0/2/ckpt_latest.zip
-
curl -L -o ckpt_latest.zip https://huggingface.co/safe-autonomous-systems/sac-RBC2D-hard-v0/resolve/main/2/ckpt_latest.zip
16.1 MB
- Xet hash:
- e1fced8116fad99808308a94e1092e4582940e0665c90d1a2e955f54868cc90c
- Size of remote file:
- 16.1 MB
- SHA256:
- ed5c5ad4b034f28b8b8cc03d8e577036a2251f67fe8de19abc47aa41a7305e80
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