#27 · Primary category： Classical Machine Learning Frameworks
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.
Project last updated：08/23/26
GitHub Stars
765
Forks
105
Contributors
37
License
MIT
Why we included this project
POMDPs.jl is the Julia community's shared interface for sequential decision problems. You describe a problem as a Markov decision process or its partially observable counterpart, hand it to a solver, and run simulations through the same API. The companion POMDPTools package supplies the usual building blocks, policies, belief updaters, distributions, and simulators, so you can go from a problem definition to a working policy without assembling every piece yourself. It also connects to other ecosystems: quickpomdps lets you define and solve problems from Python, and CommonRLInterface ties it into the JuliaReinforcementLearning packages. The interface covers both discrete and continuous spaces, and the documentation is thorough enough that teams can build reproducible decision-making experiments on it.
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