论文标题
SKRL：用于加固学习的模块化和灵活的库
skrl: Modular and Flexible Library for Reinforcement Learning
论文作者
论文摘要
SKRL是一个开源模块化库，用于用Python编写的加固学习，设计着专注于算法实现的可读性，简单性和透明度。除了支持使用OpenAi Gym和DeepMind的传统接口的支持环境外，它还提供了装载，配置和操作NVIDIA ISAAC健身房和Nvidia Omniverse Isaac Gym Gunt环境的设施。此外，它可以同时对几个具有可自定义范围的代理（所有可用环境的子集）进行培训，这些代理可能会在同一运行中共享或可能不会共享资源。可以在https://skrl.readthedocs.io上找到该库的文档，其源代码可在https://github.com/toni-sm/skrl上找到。
skrl is an open-source modular library for reinforcement learning written in Python and designed with a focus on readability, simplicity, and transparency of algorithm implementations. In addition to supporting environments that use the traditional interfaces from OpenAI Gym and DeepMind, it provides the facility to load, configure, and operate NVIDIA Isaac Gym and NVIDIA Omniverse Isaac Gym environments. Furthermore, it enables the simultaneous training of several agents with customizable scopes (subsets of environments among all available ones), which may or may not share resources, in the same run. The library's documentation can be found at https://skrl.readthedocs.io and its source code is available on GitHub at https://github.com/Toni-SM/skrl.
