markov-decision-processes
There are 349 repositories under markov-decision-processes topic.
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
Curso de Álgebra Lineal
ds4dm/ecole
Extensible Combinatorial Optimization Learning Environments
odow/SDDP.jl
Stochastic Dual Dynamic Programming in Julia
h2r/pomdp-py
A framework to build and solve POMDP problems. Documentation: https://h2r.github.io/pomdp-py/
colinskow/move37
Coding Demos from the School of AI's Move37 Course
DES-Lab/AALpy
An Automata Learning Library Written in Python
Limmen/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.
florist-notes/CS228_PGM
🌀 Stanford CS 228 - Probabilistic Graphical Models
sachinbiradar9/Markov-Decision-Processes
Implementation of value iteration algorithm for calculating an optimal MDP policy
wrighteagle2d/wrighteaglebase
WrightEagle Base Code for RoboCup Soccer Simulation 2D
OpenSourceEconomics/respy
Framework for the simulation and estimation of some finite-horizon discrete choice dynamic programming models.
lsunsi/markovjs
Reinforcement Learning in JavaScript
italohdc/LearnSnake
🐍 AI that learns to play Snake using Q-Learning (Reinforcement Learning)
amflorio/dvrp-stochastic-requests
Online algorithms for solving large-scale dynamic vehicle routing problems with stochastic requests
ImanRHT/QECO
A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing
rllab-snu/tsallis_actor_critic_mujoco
Implementation of Tsallis Actor Critic method
masouduut94/MCTS-agent-python
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 and planning problems. In this project I used a board game called "HEX" as a platform to test different simulation strategies in MCTS field.
chauvinSimon/Hierarchical-Decision-Making-for-Autonomous-Driving
Rich literature review and discussion on the implementation of "Hierarchical Decision-Making for Autonomous Driving"
thiagopbueno/awesome-probabilistic-planning
A curated list of online resources for probabilistic planning: papers, software and research groups around the world!
aws-samples/amazon-sagemaker-amazon-routing-challenge-sol
AWS Last Mile Route Sequence Optimization
sshkhr/Practical_RL
My solutions to Yandex Practical Reinforcement Learning course in PyTorch and Tensorflow
callmespring/RL-short-course
Reinforcement Learning Short Course
zafarali/emdp
Easy MDPs and grid worlds with accessible transition dynamics to do exact calculations
alexge233/relearn
A Reinforcement Learning Library for C++11/14
dsietz/test-data-generation
Test Data Generation
iisys-hof/map-matching-2
High Performance Map Matching with Markov Decision Processes (MDPs) and Hidden Markov Models (HMMs).
nasa/pymdptoolbox
Markov Decision Process (MDP) Toolbox for Python
JuliaPOMDP/QuickPOMDPs.jl
Concise and friendly interfaces for defining MDP and POMDP models for use with POMDPs.jl solvers
shehio/Everything-Financial-Engineering
Links for the most relevant topics
kevin-hanselman/grid-world-rl
Value iteration, policy iteration, and Q-Learning in a grid-world MDP.
makokal/MDPN
Unified notation for Markov Decision Processes PO(MDP)s
yudhisteer/Reinforcement-Learning-for-Supply-Chain-Management
The goal of the project was to design the logistic model of autonomous robots that would supply garment parts from the Cutting Dept to the Makeup Dept in the shortest time possible and using the most optimized path.