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
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/
A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning (DRL) for Mobile Edge Computing (MEC) | This algorithm captures the dynamics of the MEC environment by integrating ...
R.L. methods and techniques.
Coding Demos from the School of AI's Move37 Course
An Automata Learning Library Written in Python
🌲 Stanford CS 228 - Probabilistic Graphical Models
#计算机科学#A research platform to develop automated security policies using quantitative methods, e.g., optimal control, computational game theory, reinforcement learning, optimization, evolutionary methods, and...
WrightEagle Base Code for RoboCup Soccer Simulation 2D
Implementation of value iteration algorithm for calculating an optimal MDP policy
Online algorithms for solving large-scale dynamic vehicle routing problems with stochastic requests
Framework for the simulation and estimation of some finite-horizon discrete choice dynamic programming models.
#计算机科学#Reinforcement Learning in JavaScript
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 p...