Reinforcement Learning
Keras
LiteRT
PyTorch
ONNX
English
chess
deep-learning
tensorflow
self-play
mcts
Instructions to use nirajandhakal/StockZero-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use nirajandhakal/StockZero-v2 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://nirajandhakal/StockZero-v2") - Notebooks
- Google Colab
- Kaggle
update citation to display in bibtex format.
Browse files
README.md
CHANGED
|
@@ -245,6 +245,7 @@ print("Value Output:", value_output)
|
|
| 245 |
|
| 246 |
If you use this model in your research, please cite it as follows:
|
| 247 |
|
|
|
|
| 248 |
@misc{stockzero,
|
| 249 |
author = {Nirajan Dhakal},
|
| 250 |
title = {StockZero: A Self-Play Reinforcement Learning Chess Engine},
|
|
@@ -252,4 +253,5 @@ If you use this model in your research, please cite it as follows:
|
|
| 252 |
publisher = {Hugging Face},
|
| 253 |
journal = {Hugging Face Model Card},
|
| 254 |
howpublished = {\url{https://huggingface.co/nirajandhakal/StockZero-v2}}
|
| 255 |
-
}
|
|
|
|
|
|
| 245 |
|
| 246 |
If you use this model in your research, please cite it as follows:
|
| 247 |
|
| 248 |
+
```bibtex
|
| 249 |
@misc{stockzero,
|
| 250 |
author = {Nirajan Dhakal},
|
| 251 |
title = {StockZero: A Self-Play Reinforcement Learning Chess Engine},
|
|
|
|
| 253 |
publisher = {Hugging Face},
|
| 254 |
journal = {Hugging Face Model Card},
|
| 255 |
howpublished = {\url{https://huggingface.co/nirajandhakal/StockZero-v2}}
|
| 256 |
+
}
|
| 257 |
+
```
|