Interval timing is considered an important aspect of human cognition. As artificial agents increasingly function alongside humans, their interval timing abilities need 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. The suite comprises cooking scenarios involving interval timing tasks inspired by psychology literature. 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 model (CNN), a recurrent model (LSTM), and a biologically inspired recurrent model (CTRNN). The CNN model showed limited ability to perform the basic timing tasks. In contrast, both recurrent models with sufficient memory successfully performed the timing tasks, exhibited temporal control in the Fixed Interval (FI) task, and qualitatively reproduced human-like psychometric curves in the Temporal Bisection task. However, no quantitative evidence of human-like timing behavior was found. A systematic study of interval timing in artificial agents may offer complementary perspectives for improving task performance in fields like human-robot interactions (HRI), while also contributing to broader research on time perception.

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