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Welcome to the ConstrainedPlanningToolbox website!

The toolbox provides a collection of models, algorithms and tools that can be used for multi-agent planning under uncertainty in environments with resource constraints. The toolbox relies on Markov Decision Processes and Partially Observable Markov Decision Processes to model planning problems, which are augmented with additional constraints on resource consumption. This model can be used for a variety of constrained planning problems. Examples include robot navigation with limited battery capacity and planning for electric vehicle charging within congested distribution grids. More mathematical details about the models and an overview of algorithms can be found in the accompanying survey: TODO

This website provides all the information that you need to get started with constrained multi-agent planning under uncertainty. Furthermore, we encourage others to contribute to the toolbox by submitting additional algorithms and additional problem domains.