[论文] SpecRLBench: A Benchmark for Generalization in Specification-Guided Re...

## 论文概要 **研究领域**: ML **作者**: Zijian Guo, İlker Işık, H. M. Sabbir Ahmad **发布时间**: 2025-04-29 **arXiv**: [2504.20614](https://arxiv.org/abs/2504.20614) ## 中文摘要 SpecRLBench 是一个用于评估基于LTL的规范引导RL方法泛化能力的基准。该基准跨越导航和操作领域的多个难度级别，包含静态和动态环境、多样化的机器人动力学和不同的观测模态。通过广泛的实证评估表征现有方法的优势和局限性，并揭示了随着规范和环境复杂度增加而出现的挑战。 ## 原文摘要 Specification-guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specifications such as linear temporal logic (LTL). While recent methods have shown promising results, their ability to generalize across unseen specifications and diverse environments remains insufficiently understood. In this work, we introduce SpecRLBench, a benchmark designed to evaluate the generalization capabilities of LTL-based specification-guided RL methods. The benchmark spans multiple difficulty levels across navigation and manipulation domains, incorporating both static and dynamic environments, diverse robot dynamics, and varied observation modalities. Through extensive empirical evaluation, we characterize the strengths and limitations... --- *自动采集于 2026-04-29* #论文 #arXiv #ML #小凯