State-of-art simulators primarily focus on providing full-stack simulation tools or state-only parallelizability. Due to the limitation of computing resources, they have to make trade-off among photo-realism and sampling efficiency. Yet, both factors are crucial for data-driven reinforcement learning tasks. Therefore, we introduce a both rapid-rendering and photo-realistic quadrotor simulator: VisFly. VisFly offers a user-friendly framework and interfaces for users to develop or utilize. It couples differentiable dynamics and habitat-sim rendering engines, reaching frame rate of up to 10000 frame per second in cluttered environments. The simulation is wrapped as a gym environment, facilitating convenient implementation of various baseline learning algorithms. It can directly import all the open-source scene datasets compatible with habitat-sim, which provides more fair benchmarks for comparing the intelligent policy. VisFly presents a general policy architecture for tasks, and the whole framework is verified by three regular quadrotor tasks with visual observation. We will make this tool available at \url{https://github.com/SJTU-ViSYS/VisFly}.

翻译：现有最先进的模拟器主要侧重于提供全栈模拟工具或仅状态并行性。由于计算资源的限制，它们必须在照片级真实感和采样效率之间做出权衡。然而，这两个因素对于数据驱动的强化学习任务都至关重要。因此，我们介绍一种兼具快速渲染和照片级真实感的四旋翼飞行器模拟器：VisFly。VisFly提供了一个用户友好的框架和接口，供用户开发或使用。它结合了可微分动力学引擎和habitat-sim渲染引擎，在复杂环境中帧率最高可达每秒10000帧。该模拟器被封装为一个gym环境，便于便捷地实现各种基线学习算法。它可以直接导入所有与habitat-sim兼容的开源场景数据集，这为比较智能策略提供了更公平的基准。VisFly为任务提出了一种通用的策略架构，整个框架通过三个基于视觉观测的常规四旋翼任务得到了验证。我们将此工具发布于 \url{https://github.com/SJTU-ViSYS/VisFly}。