Online Dynamic Goal Recognition in Gym Environments

• URL: http://arxiv.org/abs/2509.23244v1
• Date: Sat, 27 Sep 2025 10:50:53 GMT
• Title: Online Dynamic Goal Recognition in Gym Environments
• Abstract summary: Goal Recognition (GR) is the task of inferring an agent's intended goal from partial observations of its behavior.<n>Despite recent advances in model-free GR, the field remains fragmented due to inconsistencies in benchmarks, domains, and evaluation protocols.<n>We introduce gr-libs and gr-envs, two complementary open-source frameworks that support the development, evaluation, and comparison of GR algorithms in Gym-compatible environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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