OpenRL: A Unified Reinforcement Learning Framework
Shiyu Huang Wentse Chen Yiwen Sun Fuqing Bie Wei-Wei Tu
We present OpenRL, an advanced reinforcement learning (RL) framework designed
to accommodate a diverse array of tasks, from single-agent challenges to
complex multi-agent systems. OpenRL's robust support for self-play training
empowers agents to develop advanced strategies in competitive settings.
Notably, OpenRL integrates Natural Language Processing (NLP) with RL, enabling
researchers to address a combination of RL training and language-centric tasks
effectively. Leveraging PyTorch's robust capabilities, OpenRL exemplifies
modularity and a user-centric approach. It offers a universal interface that
simplifies the user experience for beginners while maintaining the flexibility
experts require for innovation and algorithm development. This equilibrium
enhances the framework's practicality, adaptability, and scalability,
establishing a new standard in RL research. To delve into OpenRL's features, we
invite researchers and enthusiasts to explore our GitHub repository at
https://github.com/OpenRL-Lab/openrl and access our comprehensive documentation
at https://openrl-docs.readthedocs.io.