用JAX加速第一人称视觉任务的基准测试，提升算法迭代效率。
JAXenstein: Accelerated Benchmarking for First-Person Environments
- 基于JAX构建，利用渲染引擎实现快速第一人称任务仿真
- 速度比同类视觉基准快数倍，支持大规模实验
- 开源可扩展，适合研究探索与部分可观测环境的算法
强化学习算法的进步依赖于具有挑战性的基准测试。研究人员在问题设置上的迭代速度直接影响算法发展速度。现代机器学习工具（如JAX）已实现快速、可扩展的算法开发。然而，当前算法开发的主要瓶颈在于缺乏大型复杂环境用于实验。尤其在JAX强化学习生态中，尚无针对视觉第一人称任务的基准测试——这类任务对评估探索能力与应对部分可观测性至关重要。为此，我们提出JAXenstein：一个基于JAX的开源基准，集成Wolfenstein 3D渲染引擎，支持在视觉第一人称任务中进行高速、可扩展的实验。JAXenstein相比同类视觉基准速度快数倍，且易于扩展至更复杂的首人称场景。
原文摘要 · Abstract (English)
The progression of reinforcement learning algorithms have been driven by challenging benchmarks. The rate in which a researcher can iterate on a problem setting directly impacts the speed of algorithm development. Modern machine learning has produced tools that allow for fast and scalable algorithm development like the JAX library. With the availability of these tools, a serious bottleneck in algorithm development is the availability of large and complex domains for experimentation. Most notably, the JAX reinforcement learning ecosystem does not have any benchmarks that test visual first-person tasks; these domains are crucial for testing both exploration and an agent's ability to overcome partial observability. We introduce JAXenstein: an open-source JAX-based benchmark that implements the Wolfenstein 3D rendering engine for fast and scalable experimentation in visual first-person tasks. JAXenstein is several times faster than comparable vision-based benchmarks, and is easily extensible to more complex first-person domains.
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