pymdps 0.1.3

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 Maintainer-email: Duncan Eddy <[e-mail]> Requires-Python: >=3.9 Description-Content-Type: text/x-rst; charset=UTF-8 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

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Documentation: https://duncaneddy.github.io/pymdps

Rust Library Reference: https://docs.rs/crate/pymdps/latest

Source Code: https://github.com/duncaneddy/pymdps

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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:

pip install pymdps

The full documentation for PyMDPs is available at https://duncaneddy.github.io/pymdps. The documentation includes a detailed API reference, examples, and tutorials to help you get started.