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 hyperparameters.json from jackoyoungblood/DeepRL: direct link, hf CLI and curl.
- Browser
- Download file 176 Bytes
-
https://huggingface.co/jackoyoungblood/DeepRL/resolve/main/hyperparameters.json
- Command line
-
hf download hf://jackoyoungblood/DeepRL/hyperparameters.json
-
curl -L -o hyperparameters.json https://huggingface.co/jackoyoungblood/DeepRL/resolve/main/hyperparameters.json
176 Bytes
| {"h_size": 16, "n_training_episodes": 6600, "n_evaluation_episodes": 100, "max_t": 1000, "gamma": 1.0, "lr": 0.01, "env_id": "CartPole-v1", "state_space": 4, "action_space": 2} |