ObjectRL: An Object-Oriented Reinforcement Learning Codebase
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912465689772032 |
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| author | Baykal, Gulcin Akgül, Abdullah Haussmann, Manuel Tasdighi, Bahareh Werge, Nicklas Wu, Yi-Shan Kandemir, Melih |
| author_facet | Baykal, Gulcin Akgül, Abdullah Haussmann, Manuel Tasdighi, Bahareh Werge, Nicklas Wu, Yi-Shan Kandemir, Melih |
| contents | ObjectRL is an open-source Python codebase for deep reinforcement learning (RL), designed for research-oriented prototyping with minimal programming effort. Unlike existing codebases, ObjectRL is built on Object-Oriented Programming (OOP) principles, providing a clear structure that simplifies the implementation, modification, and evaluation of new algorithms. ObjectRL lowers the entry barrier for deep RL research by organizing best practices into explicit, clearly separated components, making them easier to understand and adapt. Each algorithmic component is a class with attributes that describe key RL concepts and methods that intuitively reflect their interactions. The class hierarchy closely follows common ontological relationships, enabling data encapsulation, inheritance, and polymorphism, which are core features of OOP. We demonstrate the efficiency of ObjectRL's design through representative use cases that highlight its flexibility and suitability for rapid prototyping. The documentation and source code are available at https://objectrl.readthedocs.io and https://github.com/adinlab/objectrl . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03487 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ObjectRL: An Object-Oriented Reinforcement Learning Codebase Baykal, Gulcin Akgül, Abdullah Haussmann, Manuel Tasdighi, Bahareh Werge, Nicklas Wu, Yi-Shan Kandemir, Melih Machine Learning ObjectRL is an open-source Python codebase for deep reinforcement learning (RL), designed for research-oriented prototyping with minimal programming effort. Unlike existing codebases, ObjectRL is built on Object-Oriented Programming (OOP) principles, providing a clear structure that simplifies the implementation, modification, and evaluation of new algorithms. ObjectRL lowers the entry barrier for deep RL research by organizing best practices into explicit, clearly separated components, making them easier to understand and adapt. Each algorithmic component is a class with attributes that describe key RL concepts and methods that intuitively reflect their interactions. The class hierarchy closely follows common ontological relationships, enabling data encapsulation, inheritance, and polymorphism, which are core features of OOP. We demonstrate the efficiency of ObjectRL's design through representative use cases that highlight its flexibility and suitability for rapid prototyping. The documentation and source code are available at https://objectrl.readthedocs.io and https://github.com/adinlab/objectrl . |
| title | ObjectRL: An Object-Oriented Reinforcement Learning Codebase |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.03487 |