A JuMP extension for Stochastic Dual Dynamic Programming

simple but efficient kernel regression and anomaly detection algorithms

High Performance Map Matching with Markov Decision Processes (MDPs) and Hidden Markov Models (HMMs).

Python package for the simulation and estimation of a prototypical infinite-horizon dynamic discrete choice model based on Rust (1987)

EE6204 Systems Analysis

The goal of this project is to build an RL-based algorithm that can help cab drivers maximize their profits by improving their decision-making process on the field. Taking long-term profit as the goal, a method is proposed based on reinforcement learning to optimize taxi driving strategies for profit maximization. This optimization problem is fo…

Solutions for course: "Applied Game Theory" taken at University of Novi Sad - Faculty of Technical Sciences

Markov Chain overview and their implementations in Finance

R package for Discrete-Time Markov Decision Processes

Code for the paper "An iterative sample scenario approach for the dynamic dispatch waves problem."

A fast solver for Markov Decision Processes

Learn to get started using DISCOTRESS with these tutorials! Then apply to your own Markov chains in ecology 🦜🌴 economics 💸📈 biophysics 🧬🦠 and more!

Implementations of methods in book <Reinforcement Learning: an introduction> by Sutton Barto, using Python.

Python3 library for visualizing high dimensional data.

An Introductory ML Educational Program hosted by the UofT AI Society. Topics include, Data Manipulation, Classification & Regression, Neural Networks, Computer Vision (CNNs), Natural Language Processing (RNNs), Reinforcement Learning (RL), Markov Decision Process (MDP), Genetic Algorithms, Decision Trees, K-means Clustering, Minimax, Hidden Mark…

A tool for parallel automated controller synthesis for large-scale stochastic systems.

A Yahtzee-solving python package and command line tool

I used this project to master c++. I just reimplemented monte carlo tree search agent on the game of hex with cpp.

The module covers the theory behind reinforcement learning and introduces Markov chains and Markov Decision Processes