Metadata-Version: 2.4
Name: pymdps
Version: 0.1.0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: matplotlib
Requires-Dist: plotly
Requires-Dist: pytest ; extra == 'testing'
Requires-Dist: pytest-cov ; extra == 'testing'
Requires-Dist: pytest-mock ; extra == 'testing'
Requires-Dist: mkdocs-material >=9.5.49 ; extra == 'docs'
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Requires-Dist: mkdocs-section-index >=0.3.9 ; extra == 'docs'
Requires-Dist: pymdps[testing,docs] ; extra == 'all'
Provides-Extra: testing
Provides-Extra: docs
Provides-Extra: all
License-File: LICENSE
Summary: pymdps is a Python package for working with Markov Decision Processes (MDPs) and
Partially Observable Markov Decision Processes (POMDPs). It is meant to be fast,
understandable, and easy to use.

Keywords: MDP,POMDP,Markov Decision Processes,AI,Reinforcement Learning
Home-Page: https://github.com/duncaneddy/pymdps
Author: Duncan Eddy <duncan.eddy@gmail.com>
Author-email: Duncan Eddy <duncan.eddy@gmail.com>
Maintainer-email: Duncan Eddy <duncan.eddy@gmail.com>
License: MIT
Requires-Python: >=3.9
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Documentation, https://duncaneddy.github.io/pymdps/
Project-URL: Repository, https://github.com/duncaneddy/pymdps
Project-URL: Issues, https://github.com/duncaneddy/pymdps/pulls

# PyMDPs

PyMDPs is a Python library for working with Markov Decision Processes (MDPs) and other related models like Partially Observable MDPs (POMDPs). It is designed to be easy to use and flexible, with a focus on providing a clean and intuitive API for defining and solving MDPs.

## Features

- **Simple API**: PyMDPs provides a simple and intuitive API for defining MDPs and solving them using a variety of algorithms.
- **Modular Design**: PyMDPs is designed to be modular and extensible, making it easy to add new algorithms and features.
- **Well-Documented**: PyMDPs comes with comprehensive documentation and examples to help you get started quickly.
- **Open Source**: PyMDPs is open source and released under the MIT license, so you are free to use it for any purpose.

## Getting Started

For an introduction to PyMDPs and a guide on how to get started, check out the [Getting Started](getting_started/index.md) page.

To quickly install PyMDPs, you can use pip:

```bash
pip install pymdps
```

## Documentation

The documentation is divided into the following sections:

- [Getting Started](getting_started/index.md): A guide to getting started with PyMDPs. This section covers installation instructions and a simple example of creating and solving an MDP.
- [API Reference](api_reference/index.md): The complete API reference for PyMDPs, including detailed descriptions of all classes and functions.
- [Reference](reference/index.md): This contains detailed information about the problems, algorithms, models, and other concepts used in PyMDPs.
