General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learning

Osher Elhadad; Owen Morrissey; Reuth Mirsky

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arXiv · 2505.09737

General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learning

Osher Elhadad·Owen Morrissey·Reuth Mirsky

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Abstract

Understanding an agent's goal through its behavior is a common AI problem called Goal Recognition (GR). This task becomes particularly challenging in dynamic environments where goals are numerous and ever-changing. We introduce the General Dynamic Goal Recognition (GDGR) problem, a broader definition of GR aimed at real-time adaptation of GR systems. This paper presents two novel approaches to tackle GDGR: (1) GC-AURA, generalizing to new goals using Model-Free Goal-Conditioned Reinforcement Learning, and (2) Meta-AURA, adapting to novel environments with Meta-Reinforcement Learning. We evaluate these methods across diverse environments, demonstrating their ability to achieve rapid adaptation and high GR accuracy under dynamic and noisy conditions. This work is a significant step forward in enabling GR in dynamic and unpredictable real-world environments.

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Osher Elhadad, Owen Morrissey, Reuth Mirsky. 2025-05-14. General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learning. https://arxiv.org/abs/2505.09737

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