构建开源框架，统一评估在线目标识别算法。
Online Dynamic Goal Recognition in Gym Environments
- 基于Gym环境构建动态目标识别测试平台。
- 提供可复现的基准与工具集，支持算法对比。
- 适合研究人机交互、智能监控中的目标识别者。
目标识别（GR）是从行为的部分观测中推断智能体意图目标的任务，通常在在线且单次设置下进行。尽管模型无关的GR近期取得进展，尤其在人-机器人交互、监控和辅助系统中，但该领域因基准不一致、任务域多样及评估协议差异而显得碎片化。为此，我们提出gr-libs（https://github.com/MatanShamir1/gr_libs）和gr-envs（https://github.com/MatanShamir1/gr_envs）两个互补的开源框架，支持在Gym兼容环境中开发、评估和比较GR算法。gr-libs包含基于MDP的GR基线模块化实现、诊断工具和评估工具；gr-envs提供经过定制的动态目标导向行为环境，以及确保与标准强化学习工具包兼容的封装器。二者共同构成一个标准化、可扩展且可复现的GR研究平台。两个项目均为开源，可在GitHub和PyPI获取。
原文摘要 · Abstract (English)
Goal Recognition (GR) is the task of inferring an agent's intended goal from partial observations of its behavior, typically in an online and one-shot setting. Despite recent advances in model-free GR, particularly in applications such as human-robot interaction, surveillance, and assistive systems, the field remains fragmented due to inconsistencies in benchmarks, domains, and evaluation protocols. To address this, we introduce gr-libs (https://github.com/MatanShamir1/gr_libs) and gr-envs (https://github.com/MatanShamir1/gr_envs), two complementary open-source frameworks that support the development, evaluation, and comparison of GR algorithms in Gym-compatible environments. gr-libs includes modular implementations of MDP-based GR baselines, diagnostic tools, and evaluation utilities. gr-envs provides a curated suite of environments adapted for dynamic and goal-directed behavior, along with wrappers that ensure compatibility with standard reinforcement learning toolkits. Together, these libraries offer a standardized, extensible, and reproducible platform for advancing GR research. Both packages are open-source and available on GitHub and PyPI.
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