POMCGS Algorithm Released

Robots and AI systems often operate in environments with noisy sensors and uncertain outcomes. Partially Observable Markov Decision Processes (POMDPs) offer a principled framework for such decision-making problems, but solving large and complex POMDPs to produce complete policies remains a major challenge.

To tackle such challenging POMDPs, we introduce a new algorithm called “Partially Observable Monte-Carlo Graph Search” at ICAPS 2025 and release an Julia package POMCGraphSearch.jl which implements a Monte Carlo graph search algorithm that enables to derive complete policies even for complex problems modeled with POMDPs. This package has been added into the official POMDPs.jl ecosystem as one of the recommended POMDP solvers.

For more information, please see our package on GitHub and contact Yang You.