VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.

A C++ framework for MDPs and POMDPs with Python bindings

Curso de Álgebra Lineal

Extensible Combinatorial Optimization Learning Environments

A JuMP extension for Stochastic Dual Dynamic Programming

A framework to build and solve POMDP problems. Documentation: https://h2r.github.io/pomdp-py/

Coding Demos from the School of AI's Move37 Course

An Automata Learning Library Written in Python

A research platform to develop automated security policies using quantitative methods, e.g., optimal control, computational game theory, reinforcement learning, optimization, evolutionary methods, and causal inference.

🌲 Stanford CS 228 - Probabilistic Graphical Models

Implementation of value iteration algorithm for calculating an optimal MDP policy

A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing

WrightEagle Base Code for RoboCup Soccer Simulation 2D

Framework for the simulation and estimation of some finite-horizon discrete choice dynamic programming models.

Reinforcement Learning in JavaScript

Online algorithms for solving large-scale dynamic vehicle routing problems with stochastic requests

🐍 AI that learns to play Snake using Q-Learning (Reinforcement Learning)

Monte Carlo Tree Search (MCTS) is a method for finding optimal decisions in a given domain by taking random samples in the decision space and building a search tree accordingly. It has already had a profound impact on Artificial Intelligence (AI) approaches for domains that can be represented as trees of sequential decisions, particularly games …