2017 JMLR JMLR 2017

POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty

Abstract

POMDPs.jl is an open-source framework for solving Markov decision processes (MDPs) and partially observable MDPs (POMDPs). POMDPs.jl allows users to specify sequential decision making problems with minimal effort without sacrificing the expressive nature of POMDPs, making this framework viable for both educational and research purposes. It is written in the Julia language to allow flexible prototyping and large-scale computation that leverages the high-performance nature of the language. The associated JuliaPOMDP community also provides a number of state-of-the-art MDP and POMDP solvers and a rich library of support tools to help with implementing new solvers and evaluating the solution results. The most recent version of POMDPs.jl, the related packages, and documentation can be found at github.com/ JuliaPOMDP/POMDPs.jl. [abs] [ pdf ][ bib ] [ code ] [ webpage ] © JMLR 2017. (edit, beta)

🌉 Interdisciplinary Bridge — Artificial Intelligence and Reinforcement Learning
🧭 Keyword Pioneer — policy solver
🐣 Hot Topic Early Bird — sequential decision making
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy