Implicitly Aligning Humans and Autonomous Agents through Shared Task Abstractions

• URL: http://arxiv.org/abs/2505.04579v1
• Date: Wed, 07 May 2025 17:19:17 GMT
• Title: Implicitly Aligning Humans and Autonomous Agents through Shared Task Abstractions
• Authors: Stéphane Aroca-Ouellette, Miguel Aroca-Ouellette, Katharina von der Wense, Alessandro Roncone,
• Abstract summary: We introduce HA$2$: Hierarchical Ad Hoc Agents, a framework leveraging hierarchical reinforcement learning to mimic the structured approach humans use in collaboration.<n>We evaluate HA$2$ in the Overcooked environment, demonstrating statistically significant improvement over existing baselines when paired with both unseen agents and humans.
• Score: 42.813774494968214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In collaborative tasks, autonomous agents fall short of humans in their capability to quickly adapt to new and unfamiliar teammates. We posit that a limiting factor for zero-shot coordination is the lack of shared task abstractions, a mechanism humans rely on to implicitly align with teammates. To address this gap, we introduce HA$^2$: Hierarchical Ad Hoc Agents, a framework leveraging hierarchical reinforcement learning to mimic the structured approach humans use in collaboration. We evaluate HA$^2$ in the Overcooked environment, demonstrating statistically significant improvement over existing baselines when paired with both unseen agents and humans, providing better resilience to environmental shifts, and outperforming all state-of-the-art methods.

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