Reinforcement Learning
Transformers
TensorBoard
ONNX
Taxi-v3
q-learning
custom-implementation
Eval Results (legacy)
Instructions to use jackoyoungblood/DeepRL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jackoyoungblood/DeepRL with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jackoyoungblood/DeepRL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from jackoyoungblood/DeepRL: direct link, hf CLI and curl.
- Browser
- Download file 2.66 kB
-
https://huggingface.co/jackoyoungblood/DeepRL/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://jackoyoungblood/DeepRL/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/jackoyoungblood/DeepRL/resolve/main/vec_normalize.pkl
2.66 kB
- Xet hash:
- 5633692476a6c9ee90ffb9432ace428108d3239e6a08abf4946954c9c6bfc51c
- Size of remote file:
- 2.66 kB
- SHA256:
- c9b6bf8a1fc8c375ba9b15068c6d0bab0ffd6746cef732782ddd492cb2ecd23e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.