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

WrightEagle Base Code for RoboCup Soccer Simulation 2D

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

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

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

Reinforcement Learning in JavaScript

🐍 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 …