发表机构
Ghent University; IDLab; Vrije Universiteit Amsterdam（根特大学; IDLab; 阿姆斯特丹自由大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Chronocooked基准，受Overcooked启发设计需时间决策的烹饪场景，用三类模型做基线评估，旨在强调人工智能体需融入时间感知与处理能力。
AI 中文摘要
本文提出Chronocooked，这是一个用于研究强化学习（RL）智能体隐式间隔计时的强化学习基准测试套件。该套件受《胡闹厨房》（Overcooked）启发，包含需要时间决策的烹饪场景。任务和奖励函数的设计使得时间信息不可观测，但对最优性能至关重要。环境被刻意保持简单，以支持可控实验和符合生物学的模型。评估指标旨在揭示RL智能体计时能力的局限性，我们报告了使用非循环模型、循环模型和符合生物学的模型的基线结果。这项工作的最终目的是强调，在设计用于人机交互以及在依赖时间的人类社会中部署的人工智能体时，需要融入时间感知和时间处理能力。
英文摘要
Interval timing is extensively studied as an important aspect of human behaviour. As artificial agents are increasingly designed to function alongside humans, their interval timing abilities also needs to be studied. However, research in this area remains limited and scattered. This paper presents Chronocooked, a reinforcement learning (RL) benchmark environment that enables a systematic study of interval timing abilities in RL agents. Inspired by Overcooked, the suite comprises cooking scenarios involving interval timing tasks drawn from the psychology literature. The tasks and reward functions are designed such that temporal information is unobserved but critical for optimal performance. The environment is intentionally kept simple to enable controlled experiments and support biologically plausible models. Each task is accompanied by evaluation metrics to study different aspects of interval timing in RL agents, namely, task performance, human-like timing and scalability. We report baselines using a non-recurrent architecture (CNN), a recurrent architecture (LSTM), and a biologically inspired recurrent architecture (CTRNN). The baseline model analysis shows that, although RL agents can successfully perform time-dependent tasks, they do not necessarily process and perceive time in the same way as humans. Understanding these differences is important for anticipating their impact on human-robot interactions (HRI).
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