Metadata-Version: 2.4
Name: atarihelpers
Version: 0.0.11
Summary: A curated collection of helpers needed for Atari DRL environment management.
Author-email: Taha Shieenavaz <tahashieenavaz@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Taha Shieenavaz
        
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Project-URL: Homepage, https://github.com/tahashieenavaz/atarihelpers
Project-URL: Repository, https://github.com/tahashieenavaz/atarihelpers
Project-URL: Documentation, https://github.com/tahashieenavaz/atarihelpers#readme
Keywords: reinforcement learning,image,atari,helpers
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: opencv-python
Dynamic: license-file

# atarihelpers 🎮

A tiny toolkit for tidying up Atari observations before they hit your RL agent. Keep installs light, configs simple, and pre-processing consistent.

- 🚀 Quick preprocessing for Atari frames (grayscale + resize)
- 🧰 Single helper focused on DRL needs; no extra baggage
- ✅ Tested on Python 3.9+ with NumPy + OpenCV

## Installation

```bash
pip install atarihelpers
```

## Usage

```python
import gymnasium as gym
from atarihelpers import process_state

env = gym.make("ALE/Pong-v5")
state, _ = env.reset()

processed = process_state(
    state,
    image_size=84,   # target square size
    grayscale=True,  # convert to single channel
    resize=True,     # skip if you want original resolution
)
```

📝 Input should be a NumPy array shaped `(H, W, C)` in BGR order (OpenCV style). The helper returns the processed NumPy array ready for stacking or feeding to your model.
