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

An extension of POMDPs.jl for Mixed Observability MDPs

Monte Carlo Tree Search for Markov decision processes using the POMDPs.jl framework

Simple particle filter implementation in Julia - works with POMDPs.jl models or others.

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

Implementation of the Deep Q-learning algorithm to solve MDPs

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

Compressed belief-state MDPs in Julia for reinforcement learning and sequential decision making. Part of the POMDPs.jl community.