构建游戏化基准测试，评估智能体隐式时间感知能力。
Chronocooked: A Benchmark for Implicit Interval Timing in Reinforcement Learning Agents
- 设计需隐式计时的烹饪任务，时间信息不可见但影响表现。
- 非循环、循环及生物合理模型均在时间判断上表现不佳。
- 适合研究人机交互中时间感知的智能体开发。
本文提出Chronocooked，一个用于研究强化学习智能体隐式间隔时间感知的基准测试套件。受Overcooked启发，该套件包含需进行时间决策的烹饪场景，任务与奖励函数设计为时间信息虽未被观测却对最优表现至关重要。环境刻意保持简单，以支持可控实验和生物合理模型。评估指标旨在揭示智能体在时间能力上的局限性，并报告了非循环、循环及生物合理模型的基线表现。本工作旨在强调在面向人类交互与时间依赖社会部署的人工智能系统中，融入时间感知与时间处理的必要性。
原文摘要 · Abstract (English)
This paper presents Chronocooked, a reinforcement learning (RL) benchmark suite for studying implicit interval timing in RL agents. Inspired by Overcooked, the suite comprises cooking scenarios that require temporal decision making. The tasks and reward functions are designed such that temporal information is unobserved yet critical for optimal performance. The environment is intentionally kept simple to enable controlled experiments and support biologically plausible models. Evaluation metrics are designed to expose limitations in timing abilities of RL agents, and we report baselines using a non-recurrent, a recurrent, and a biologically plausible model. This work ultimately aims to underscore the need to incorporate time perception and temporal processing in artificial agents designed for human robot interaction and deployment in time dependent human societies.
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