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CaRL: Learning Scalable Planning Policies with Simple Rewards

Paper

This repository contains the first public codebase for doing Reinforcement Learning with the CARLA leaderboard 2.0 and nuPlan. We provide pre-trained model weights for CaRL which is the best open-source RL planner on longest6 v2 and nuPlan. Additionally, we provide reproductions of the popular CARLA planners Roach, PlanT and Think2Drive for the CARLA leaderboard 2.0. The code and documentation for the respective simulators can be found in the CARLA folder and the nuPlan_folder.

License

The original code in this repository is provided under the Civil-M license, which is a variant of the MIT license that bans dual-use. The license contains a partial copyleft which requires derivative work to include the civil clause in their license. For further information see the accompaning documentation on Civil Software Licenses.

Citation

If you find the repo useful, please consider giving it a star 🌟. To cite the paper please use the following bibtex:

@InProceedings{Jaeger2025CoRL, author = {Bernhard Jaeger and Daniel Dauner and Jens Beißwenger and Simon Gerstenecker and Kashyap Chitta and Andreas Geiger}, title = {CaRL: Learning Scalable Planning Policies with Simple Rewards}, booktitle = {Proc. of the Conf. on Robot Learning (CoRL)}, year = {2025}, }

Acknowledgements

The original code in this repository was written by Bernhard Jaeger, Daniel Dauner, Jens Beißwenger and Simon Gerstenecker. Kashyap Chitta and Andreas Geiger have contributed as technical advisors.

Code like this is build on the shoulders of many other open source repositories. Particularly, we would like to thank the following repositories for their contributions:

We also thank the creators of the numerous libraries we use. Complex projects like this would not be feasible without your contribution.

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Repository files navigation
 CaRL: Learning Scalable Planning Policies with Simple Rewards 
 Paper
This repository contains the first public codebase for doing Reinforcement Learning with the CARLA leaderboard 2.0 and nuPlan. We provide pre-trained model weights for CaRL which is the best open-source RL planner on longest6 v2 and nuPlan. Additionally, we provide reproductions of the popular CARLA planners Roach, PlanT and Think2Drive for the CARLA leaderboard 2.0.
The code and documentation for the respective simulators can be found in the CARLA folder and the nuPlan_folder.
License
The original code in this repository is provided under the Civil-M license, which is a variant of the MIT license that bans dual-use. The license contains a partial copyleft which requires derivative work to include the civil clause in their license. For further information see the accompaning documentation on Civil Software Licenses.
Citation
If you find the repo useful, please consider giving it a star 🌟.
To cite the paper please use the following bibtex:
@InProceedings{Jaeger2025CoRL, author = {Bernhard Jaeger and Daniel Dauner and Jens Beißwenger and Simon Gerstenecker and Kashyap Chitta and Andreas Geiger}, title = {CaRL: Learning Scalable Planning Policies with Simple Rewards}, booktitle = {Proc. of the Conf. on Robot Learning (CoRL)}, year = {2025}, }
Acknowledgements
The original code in this repository was written by Bernhard Jaeger, Daniel Dauner, Jens Beißwenger and Simon Gerstenecker. Kashyap Chitta and Andreas Geiger have contributed as technical advisors.
Code like this is build on the shoulders of many other open source repositories.
Particularly, we would like to thank the following repositories for their contributions:
We also thank the creators of the numerous libraries we use. Complex projects like this would not be feasible without your contribution.
About
[CoRL 2025] CaRL: Learning Scalable Planning Policies with Simple Rewards
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Repository files navigation
 CaRL: Learning Scalable Planning Policies with Simple Rewards 
 Paper
This repository contains the first public codebase for doing Reinforcement Learning with the CARLA leaderboard 2.0 and nuPlan. We provide pre-trained model weights for CaRL which is the best open-source RL planner on longest6 v2 and nuPlan. Additionally, we provide reproductions of the popular CARLA planners Roach, PlanT and Think2Drive for the CARLA leaderboard 2.0.
The code and documentation for the respective simulators can be found in the CARLA folder and the nuPlan_folder.
License
The original code in this repository is provided under the Civil-M license, which is a variant of the MIT license that bans dual-use. The license contains a partial copyleft which requires derivative work to include the civil clause in their license. For further information see the accompaning documentation on Civil Software Licenses.
Citation
If you find the repo useful, please consider giving it a star 🌟.
To cite the paper please use the following bibtex:
@InProceedings{Jaeger2025CoRL, author = {Bernhard Jaeger and Daniel Dauner and Jens Beißwenger and Simon Gerstenecker and Kashyap Chitta and Andreas Geiger}, title = {CaRL: Learning Scalable Planning Policies with Simple Rewards}, booktitle = {Proc. of the Conf. on Robot Learning (CoRL)}, year = {2025}, }
Acknowledgements
The original code in this repository was written by Bernhard Jaeger, Daniel Dauner, Jens Beißwenger and Simon Gerstenecker. Kashyap Chitta and Andreas Geiger have contributed as technical advisors.
Code like this is build on the shoulders of many other open source repositories.
Particularly, we would like to thank the following repositories for their contributions:
We also thank the creators of the numerous libraries we use. Complex projects like this would not be feasible without your contribution.
About
[CoRL 2025] CaRL: Learning Scalable Planning Policies with Simple Rewards
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Repository files navigation
 CaRL: Learning Scalable Planning Policies with Simple Rewards 
 Paper
This repository contains the first public codebase for doing Reinforcement Learning with the CARLA leaderboard 2.0 and nuPlan. We provide pre-trained model weights for CaRL which is the best open-source RL planner on longest6 v2 and nuPlan. Additionally, we provide reproductions of the popular CARLA planners Roach, PlanT and Think2Drive for the CARLA leaderboard 2.0.
The code and documentation for the respective simulators can be found in the CARLA folder and the nuPlan_folder.
License
The original code in this repository is provided under the Civil-M license, which is a variant of the MIT license that bans dual-use. The license contains a partial copyleft which requires derivative work to include the civil clause in their license. For further information see the accompaning documentation on Civil Software Licenses.
Citation
If you find the repo useful, please consider giving it a star 🌟.
To cite the paper please use the following bibtex:
@InProceedings{Jaeger2025CoRL, author = {Bernhard Jaeger and Daniel Dauner and Jens Beißwenger and Simon Gerstenecker and Kashyap Chitta and Andreas Geiger}, title = {CaRL: Learning Scalable Planning Policies with Simple Rewards}, booktitle = {Proc. of the Conf. on Robot Learning (CoRL)}, year = {2025}, }
Acknowledgements
The original code in this repository was written by Bernhard Jaeger, Daniel Dauner, Jens Beißwenger and Simon Gerstenecker. Kashyap Chitta and Andreas Geiger have contributed as technical advisors.
Code like this is build on the shoulders of many other open source repositories.
Particularly, we would like to thank the following repositories for their contributions:
We also thank the creators of the numerous libraries we use. Complex projects like this would not be feasible without your contribution.
About
[CoRL 2025] CaRL: Learning Scalable Planning Policies with Simple Rewards
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Repository files navigation
 CaRL: Learning Scalable Planning Policies with Simple Rewards 
 Paper
This repository contains the first public codebase for doing Reinforcement Learning with the CARLA leaderboard 2.0 and nuPlan. We provide pre-trained model weights for CaRL which is the best open-source RL planner on longest6 v2 and nuPlan. Additionally, we provide reproductions of the popular CARLA planners Roach, PlanT and Think2Drive for the CARLA leaderboard 2.0.
The code and documentation for the respective simulators can be found in the CARLA folder and the nuPlan_folder.
License
The original code in this repository is provided under the Civil-M license, which is a variant of the MIT license that bans dual-use. The license contains a partial copyleft which requires derivative work to include the civil clause in their license. For further information see the accompaning documentation on Civil Software Licenses.
Citation
If you find the repo useful, please consider giving it a star 🌟.
To cite the paper please use the following bibtex:
@InProceedings{Jaeger2025CoRL, author = {Bernhard Jaeger and Daniel Dauner and Jens Beißwenger and Simon Gerstenecker and Kashyap Chitta and Andreas Geiger}, title = {CaRL: Learning Scalable Planning Policies with Simple Rewards}, booktitle = {Proc. of the Conf. on Robot Learning (CoRL)}, year = {2025}, }
Acknowledgements
The original code in this repository was written by Bernhard Jaeger, Daniel Dauner, Jens Beißwenger and Simon Gerstenecker. Kashyap Chitta and Andreas Geiger have contributed as technical advisors.
Code like this is build on the shoulders of many other open source repositories.
Particularly, we would like to thank the following repositories for their contributions:
We also thank the creators of the numerous libraries we use. Complex projects like this would not be feasible without your contribution.
About
[CoRL 2025] CaRL: Learning Scalable Planning Policies with Simple Rewards
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Repository files navigation
 CaRL: Learning Scalable Planning Policies with Simple Rewards 
 Paper
This repository contains the first public codebase for doing Reinforcement Learning with the CARLA leaderboard 2.0 and nuPlan. We provide pre-trained model weights for CaRL which is the best open-source RL planner on longest6 v2 and nuPlan. Additionally, we provide reproductions of the popular CARLA planners Roach, PlanT and Think2Drive for the CARLA leaderboard 2.0.
The code and documentation for the respective simulators can be found in the CARLA folder and the nuPlan_folder.
License
The original code in this repository is provided under the Civil-M license, which is a variant of the MIT license that bans dual-use. The license contains a partial copyleft which requires derivative work to include the civil clause in their license. For further information see the accompaning documentation on Civil Software Licenses.
Citation
If you find the repo useful, please consider giving it a star 🌟.
To cite the paper please use the following bibtex:
@InProceedings{Jaeger2025CoRL, author = {Bernhard Jaeger and Daniel Dauner and Jens Beißwenger and Simon Gerstenecker and Kashyap Chitta and Andreas Geiger}, title = {CaRL: Learning Scalable Planning Policies with Simple Rewards}, booktitle = {Proc. of the Conf. on Robot Learning (CoRL)}, year = {2025}, }
Acknowledgements
The original code in this repository was written by Bernhard Jaeger, Daniel Dauner, Jens Beißwenger and Simon Gerstenecker. Kashyap Chitta and Andreas Geiger have contributed as technical advisors.
Code like this is build on the shoulders of many other open source repositories.
Particularly, we would like to thank the following repositories for their contributions:
We also thank the creators of the numerous libraries we use. Complex projects like this would not be feasible without your contribution.
About
[CoRL 2025] CaRL: Learning Scalable Planning Policies with Simple Rewards
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Repository files navigation
 CaRL: Learning Scalable Planning Policies with Simple Rewards 
 Paper
This repository contains the first public codebase for doing Reinforcement Learning with the CARLA leaderboard 2.0 and nuPlan. We provide pre-trained model weights for CaRL which is the best open-source RL planner on longest6 v2 and nuPlan. Additionally, we provide reproductions of the popular CARLA planners Roach, PlanT and Think2Drive for the CARLA leaderboard 2.0.
The code and documentation for the respective simulators can be found in the CARLA folder and the nuPlan_folder.
License
The original code in this repository is provided under the Civil-M license, which is a variant of the MIT license that bans dual-use. The license contains a partial copyleft which requires derivative work to include the civil clause in their license. For further information see the accompaning documentation on Civil Software Licenses.
Citation
If you find the repo useful, please consider giving it a star 🌟.
To cite the paper please use the following bibtex:
@InProceedings{Jaeger2025CoRL, author = {Bernhard Jaeger and Daniel Dauner and Jens Beißwenger and Simon Gerstenecker and Kashyap Chitta and Andreas Geiger}, title = {CaRL: Learning Scalable Planning Policies with Simple Rewards}, booktitle = {Proc. of the Conf. on Robot Learning (CoRL)}, year = {2025}, }
Acknowledgements
The original code in this repository was written by Bernhard Jaeger, Daniel Dauner, Jens Beißwenger and Simon Gerstenecker. Kashyap Chitta and Andreas Geiger have contributed as technical advisors.
Code like this is build on the shoulders of many other open source repositories.
Particularly, we would like to thank the following repositories for their contributions:
We also thank the creators of the numerous libraries we use. Complex projects like this would not be feasible without your contribution.
About
[CoRL 2025] CaRL: Learning Scalable Planning Policies with Simple Rewards
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 CaRL: Learning Scalable Planning Policies with Simple Rewards 
 Paper
This repository contains the first public codebase for doing Reinforcement Learning with the CARLA leaderboard 2.0 and nuPlan. We provide pre-trained model weights for CaRL which is the best open-source RL planner on longest6 v2 and nuPlan. Additionally, we provide reproductions of the popular CARLA planners Roach, PlanT and Think2Drive for the CARLA leaderboard 2.0.
The code and documentation for the respective simulators can be found in the CARLA folder and the nuPlan_folder.
License
The original code in this repository is provided under the Civil-M license, which is a variant of the MIT license that bans dual-use. The license contains a partial copyleft which requires derivative work to include the civil clause in their license. For further information see the accompaning documentation on Civil Software Licenses.
Citation
If you find the repo useful, please consider giving it a star 🌟.
To cite the paper please use the following bibtex:
@InProceedings{Jaeger2025CoRL, author = {Bernhard Jaeger and Daniel Dauner and Jens Beißwenger and Simon Gerstenecker and Kashyap Chitta and Andreas Geiger}, title = {CaRL: Learning Scalable Planning Policies with Simple Rewards}, booktitle = {Proc. of the Conf. on Robot Learning (CoRL)}, year = {2025}, }
Acknowledgements
The original code in this repository was written by Bernhard Jaeger, Daniel Dauner, Jens Beißwenger and Simon Gerstenecker. Kashyap Chitta and Andreas Geiger have contributed as technical advisors.
Code like this is build on the shoulders of many other open source repositories.
Particularly, we would like to thank the following repositories for their contributions:
We also thank the creators of the numerous libraries we use. Complex projects like this would not be feasible without your contribution.
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