# Online Dynamic Goal Recognition in Gym Environments

arXiv: https://arxiv.org/abs/2509.23244 · HTML: https://mlsanity.com/paper/2509.23244/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Goal-Conditioned Reinforcement Learning: Problems and Solutions](https://mlsanity.com/paper/2201.08299/) (2022)
- [Landmark-Based Approaches for Goal Recognition as Planning](https://mlsanity.com/paper/1904.11739/) (2019)
- [Interpretable Sequence Classification via Discrete Optimization](https://mlsanity.com/paper/2010.02819/) (2020)
- [Heuristic Online Goal Recognition in Continuous Domains](https://mlsanity.com/paper/1709.09839/) (2017)
- [Goal Recognition as Reinforcement Learning](https://mlsanity.com/paper/2202.06356/) (2022)
- [Goal Recognition as a Deep Learning Task: the GRNet Approach](https://mlsanity.com/paper/2210.02377/) (2022)
- [ODGR: Online Dynamic Goal Recognition](https://mlsanity.com/paper/2407.16220/) (2024)
- [Goal Recognition using Actor-Critic Optimization](https://mlsanity.com/paper/2501.01463/) (2024)

## Abstract (arXiv)

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.