VIP cheatsheets for Stanford's CS 221 Artificial Intelligence
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Updated
Dec 17, 2019
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.
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)
A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing
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 …
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