Metadata-Version: 2.3
Name: neodynamics
Version: 0.1.4
Summary: A package for dynamic systems simulation and Docker management
License: MIT
Requires-Python: >=3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: docker (>=6.0.0)
Requires-Dist: grpcio-tools
Requires-Dist: gymnasium
Requires-Dist: msgpack
Description-Content-Type: text/markdown

# NeoDynamics

Making Reinforcement Learning accessible to everyone.

## Overview

NeoDynamics is an ambitious project aimed at democratizing Reinforcement Learning (RL). Our mission is to provide intuitive, efficient, and powerful tools that make RL accessible to developers, researchers, and enthusiasts of all skill levels.

### Key Features (Coming Soon)
- 🎯 Simple, intuitive API for RL algorithms
- 🚀 High-performance implementations
- 📚 Comprehensive documentation and tutorials
- 🛠️ Ready-to-use examples and templates
- 🔧 Flexible and extensible architecture
- 📊 Built-in visualization tools

## Why NeoDynamics?

Reinforcement Learning has shown tremendous potential in solving complex decision-making problems, from game playing to robotics. However, the barrier to entry remains high due to:
- Complex mathematical concepts
- Difficult-to-implement algorithms
- Lack of standardized tools
- High computational requirements

NeoDynamics aims to address these challenges by providing a unified, user-friendly framework that makes RL accessible while maintaining the power and flexibility needed for advanced applications.

## Building and Publishing
1. Install build tools:
   ```bash
   pip install build twine
   ```

1. Clean previous builds:
   ```bash
   rm -rf dist/ build/ *.egg-info
   ```

2. Build the package:
   ```bash
   python -m build
   ```

3. Upload to PyPI (ensure you have a PyPI account):
   ```bash
   # Production PyPI
   python -m twine upload dist/*
   ```

Users can then install your package using:
```bash
pip install neodynamics
```
