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DOI: 10.4121/14bd06bf-7168-4791-94e6-a33c4279d466

Filippo Airaldi (2025): Source code for the publication: Probabilistically safe and efficient model-based Reinforcement Learning. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/14bd06bf-7168-4791-94e6-a33c4279d466.v1

Categories

• Electrical and Electronic Engineering
• Other Engineering
• Engineering

Keywords

Control Barrier Function Model Predictive Control Reinforcement Learning Terminal Cost Approximation

Licence

MIT

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by Filippo Airaldi

Source code for the implementation and simulation of Model Predictive Control-based RL algorithms leveraging probabilistic Control Barrier Function formulations to enforce safety of state trajectories with arbitrary probability

History

• 2025-04-07 first online, published, posted

Publisher

4TU.ResearchData

Format

source code (.py) and compressed simulation results (.xz)

Associated peer-reviewed publication

Probabilistically safe and efficient model-based Reinforcement Learning

References

• https://arxiv.org/abs/2504.00626
• https://github.com/FilippoAiraldi/mpcrl-cbf

Code hosting project url

https://github.com/FilippoAiraldi/mpcrl-cbf

Funding

• CLariNet (grant code 101018826) [more info...] European Research Council

Organizations

TU Delft, Faculty of Mechanical Engineering, Delft Center for Systems and Control