Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

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

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

POMDPs in Python.

Implementation of the Deep Q-learning algorithm to solve MDPs

Online solver based on Monte Carlo tree search for POMDPs with continuous state, action, and observation spaces.

A gallery of POMDPs.jl problems

The PO-UCT algorithm (aka POMCP) implemented in Julia

Concise and friendly interfaces for defining MDP and POMDP models for use with POMDPs.jl solvers

Interface for defining discrete and continuous-space MDPs and POMDPs in python. Compatible with the POMDPs.jl ecosystem.

Pytorch code for "Learning Belief Representations for Imitation Learning in POMDPs" (UAI 2019)

Adaptive stress testing of black-box systems within POMDPs.jl

Thompson Sampling based Monte Carlo Tree Search for MDPs and POMDPs

Julia Implementation of the POMCP algorithm for solving POMDPs

Sparse Particle Tree POMDP Solvers

A collection of pomdp domains in robotics.

A Julia package for solving POMDPs with belief compression. Part of the POMDPs.jl community.

The goal of the project is to make a robot plan its path from a source to the destination and reach the destination only by evidence and its previous transition.

POMDP-based decision-making technique for Social Robots using ROS, Python and Julia