Dynamic Programming Python Packages

kmeans1d

A Python package for optimal 1D k-means clustering.

symbolicdsge

A linear DSGE engine with a fully symbolic and modifiable model backend. Includes symbolic regression-based equation augmentation capabilities.

slambuc

Serverless Layout Adaptation with Memory-Bounds and User Constraints

flowty

Network optimisation solver

django-tasks-manager

Django Tasks Manager - Simple Celery Integration | AppSeed

shapeless

A Fluid, PolyMorphic,and shapeless Types Package that activates radically flexiblity and simplicity in your programs

tianyancha

Tianyancha SDK for python, which is based on https://open.tianyancha.com/

pyscm-ml

pySCM: The Set Covering Machine in Python

shortestpaths

Bidirectional k-shortest paths with DP

pycair

Content aware image resizing

gdtw

GDTW is a Python/C++ library that performs dynamic time warping. It is based on a paper by Dave Deriso and Stephen Boyd.

gym-classics

Classic environments for reinforcement learning and dynamic programming, implemented in OpenAI Gym and Gymnasium.

pyro-udes

An object-based toolbox for robot dynamic simulation, analysis, control and planning.

genes2genes

A tool for aligning gene expression trajectories of single-cell reference and query systems

liquiditylife

Life-cycle portfolio choice with liquidity risk, labor-income risk, and consumption adjustment frictions

pattern-clustering

The pattern clustering tool groups similar input log lines according to a set of predefined regular expressions.

upgrade-policy-optimizer

A Python library for solving finite Markov Decision Processes using value iteration

cvrp

A Python implementation for solving the Capacitated Vehicle Routing Problem (CVRP) using heuristic and metaheuristic algorithms

pythorn

A Python module for all the data structures and algorithms.

yahtzee-solve

A Yahtzee-solving python package and command line tool

tabular-rl

A simple Reinforcement Learning framework to decouple agents from environments in discrete-state settings with implementations of double Q-learning and policy iteration through dynamic programming.