markov-decision-processes 开源项目推荐（483 个）

afshinea /stanford-cs-221-artificial-intelligence

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

sudharsan13296 /Hands-On-Reinforcement-Learning-With-Python

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

JuliaPOMDP /POMDPs.jl

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

Svalorzen /AI-Toolbox

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

joanby /curso-algebra-lineal

线性代数课程

ds4dm /ecole

可扩展的组合优化学习环境

odow /SDDP.jl

A JuMP extension for Stochastic Dual Dynamic Programming

ImanRHT /QECO

基于深度强化学习（DRL）的面向QoE计算卸载算法，适用于移动边缘计算（MEC）|该算法通过将双重深度Q网络（D3QN）模型与长短期记忆（LSTM）网络集成，捕捉了MEC环境的动态。

h2r /pomdp-py

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

ivanbelenky /RL

R.L. methods and techniques.

florist-notes /CS228_PGM

🌲 Stanford CS 228 - Probabilistic Graphical Models

colinskow /move37

Coding Demos from the School of AI's Move37 Course

Kim-Hammar /csle

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.

wrighteagle2d /wrighteaglebase

WrightEagle Base Code for RoboCup Soccer Simulation 2D

sachinbiradar9 /Markov-Decision-Processes

Implementation of value iteration algorithm for calculating an optimal MDP policy

callmespring /RL-short-course

Reinforcement Learning Short Course

addy90 /map-matching-2

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

amflorio /dvrp-stochastic-requests

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

aws-samples /amazon-sagemaker-amazon-routing-challenge-sol

AWS 最后一公里路由序列优化

OpenSourceEconomics /respy

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