Metadata-Version: 2.4
Name: gymnasium_sudoku
Version: 0.1.14
Summary: A Sudoku environment for Reinforcement Learning research
Author-email: adeottidev@gmail.com
License: The MIT License
        
        Copyright (c) 2025 Author(s)
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Project-URL: Homepage, https://github.com/adeotti/Gymnasium-Sudoku
Project-URL: Repository, https://github.com/adeotti/Gymnasium-Sudoku
Keywords: Reinforcement Learning,game,RL,AI,gymnasium,Sudoku
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: gymnasium>=1.1.1
Requires-Dist: numpy>=1.25.2
Requires-Dist: PySide6>=6.7.2
Requires-Dist: typing-extensions>=4.14.0
Requires-Dist: cloudpickle>=3.1.1
Dynamic: license-file

>[!warning]
>  Under active development...Expect frequent code changes....

```
pip install gymnasium_sudoku==0.1.11
```

```python
import gymnasium_sudoku
import gymnasium as gym

env = gym.make("sudoku-v0",render_mode="human",horizon=300)
env.reset()
steps = 100

for n in range(steps):
    env.step(env.action_space.sample())
    env.render() 
```

And for training : 

```python
env = gym.make("sudoku-v0",horizon=300)
# It is better not to call .render() during training 
```
