Metadata-Version: 2.4
Name: atarihelpers
Version: 0.0.13
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
Requires-Dist: gymnasium
Requires-Dist: ale_py
Dynamic: license-file

# Atari Helpers

Lightweight utilities for Atari reinforcement learning: launch environments fast, capture episodic video if you want, and preprocess frames so they are ready for your agent. All signal, no cargo cult. 🎯

- 🎮 `make_environment`: spin up Gymnasium Atari envs with optional video recording
- 🖼️ `process_state`: grayscale + resize frames for downstream stacks
- 🧰 Zero-fluff dependency set (Gymnasium, ALE-Py, NumPy, OpenCV)

## Installation

```bash
pip install atarihelpers
```

## Quickstart

```python
from atarihelpers import make_environment, process_state

env = make_environment(
    "ALE/Pong-v5",
    record=True,       # 🎥 save videos to ./videos
    record_every=25,   # capture every 25th episode
)

state, _ = env.reset()
processed = process_state(
    state,
    image_size=84,     # target square size
    grayscale=True,    # convert to single channel
    resize=True,       # keep original resolution if False
)
```

Note: inputs should be NumPy arrays shaped `(H, W, C)` in BGR order (OpenCV style). Returns the processed NumPy array ready for stacking or feeding to your model.
