XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
This provides a unified tool for researchers and practitioners in reinforcement learning, though it is incremental as it builds on existing libraries and algorithms.
The authors introduced XuanCe, a deep reinforcement learning library compatible with multiple frameworks, offering over 40 algorithms and demonstrating strong performance in benchmarks on environments like MuJoCo and StarCraftII.
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 https://github.com/agi-brain/xuance.git.