ObjectRL: An Object-Oriented Reinforcement Learning Codebase
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Abstract (arXiv)
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 .
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References · 16
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
- 2021Randomized Ensembled Double Q-Learning: Learning Fast Without a Modelcited by 278
- 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 139
- 2024Group Robust Preference Optimization in Reward-free RLHFcited by 94
- 2019Better Exploration with Optimistic Actor-Criticcited by 88
- 2023TorchRL: A data-driven decision-making library for PyTorchcited by 59
- 2020MushroomRL: Simplifying Reinforcement Learning Researchcited by 50
- 2023Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priorscited by 36
- 2022skrl: Modular and Flexible Library for Reinforcement Learningcited by 35
4 more
- 2024Exploration and Anti-Exploration with Distributional Random Network Distillationcited by 32
- 2023Pearl: A Production-ready Reinforcement Learning Agentcited by 9
- 2023RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Controlcited by 4
- 2024Deep Exploration with PAC-Bayescited by 4
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