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

POMDPs in Python.

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

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)

Thompson Sampling based Monte Carlo Tree Search for MDPs and POMDPs

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

Julia Implementation of the POMCP algorithm for solving POMDPs

Sparse Particle Tree POMDP Solvers

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.

Compressed belief-state MDPs in Julia compatible with POMDPs.jl

A collection of pomdp domains in robotics.

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