A Comprehensive and Unified Deep Reinforcement Learning Library

In this article, we introduce XuanCe, a groundbreaking deep reinforcement learning (DRL) library that promises to revolutionize the field. Designed to be compatible with popular frameworks such as PyTorch, TensorFlow, and MindSpore, XuanCe offers a wide range of functionalities and aims to be a comprehensive solution for researchers and practitioners in the DRL community.

One of the key features of XuanCe is its extensive collection of over 40 classical DRL and multi-agent DRL algorithms. Whether you are interested in classical reinforcement learning methods like Q-learning or cutting-edge multi-agent algorithms, XuanCe has you covered. Moreover, XuanCe is designed to be highly flexible, allowing researchers to easily incorporate new algorithms and environments into the library.

Another major advantage of XuanCe is its versatility. The library supports CPU, GPU, and Ascend, making it accessible to a wide range of computing resources. Additionally, XuanCe can be executed on various operating systems, including Ubuntu, Windows, MacOS, and EulerOS. This versatility ensures that researchers can seamlessly integrate XuanCe into their existing workflows, regardless of their preferred platform.

To validate the performance of XuanCe, extensive benchmarks have been conducted on popular environments such as MuJoCo, Atari, and the StarCraftII multi-agent challenge. The results have been nothing short of impressive, showcasing the library’s ability to achieve state-of-the-art performance across a wide range of tasks and domains.

Importantly, XuanCe is an open-source project, which means that it is freely available for anyone to access and use. The code can be found on GitHub at https://github.com/agi-brain/xuance.git. This open-source nature not only fosters collaboration among researchers but also allows the community to contribute to the library’s improvement and expansion.

In conclusion, XuanCe is a comprehensive and unified DRL library that brings together the best of various frameworks and algorithms. With its compatibility, flexibility, and impressive performance, XuanCe has the potential to propel the field of deep reinforcement learning forward and empower researchers and practitioners to tackle increasingly complex challenges.

In this paper, we present XuanCe, a comprehensive and unified deep
reinforcement learning (DRL) library designed to be compatible with PyTorch,
TensorFlow, and MindSpore. XuanCe offers a wide range of functionalities,
including over 40 classical DRL and multi-agent DRL algorithms, with the
flexibility to easily incorporate new algorithms and environments. It is a
versatile DRL library that supports CPU, GPU, and Ascend, and can be executed
on various operating systems such as Ubuntu, Windows, MacOS, and EulerOS.
Extensive benchmarks conducted on popular environments including MuJoCo, Atari,
and StarCraftII multi-agent challenge demonstrate the library’s impressive
performance. XuanCe is open-source and can be accessed at

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