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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