Reinforcement Learning
Keras
English
tensoraerospace
control
ihdp
aerospace
f16
gymnasium
tensorflow
Eval Results (legacy)
Instructions to use TensorAeroSpace/ihdp-f16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use TensorAeroSpace/ihdp-f16 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://TensorAeroSpace/ihdp-f16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0dc081b60b2b6d6036b063e019bc80cd84f0e5ccd3087a71f0697b5fb9f860a3
- Size of remote file:
- 152 Bytes
- SHA256:
- 9d51b1f49a776606430a52f4130bd757538b22ea357d2d376eb9e00fc8f10f5e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.