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.
Learned reward models allow reinforcement-learning agents to use demonstrations, implicit feedback, safety constraints, social norms, or other sources that do not provide a complete hand-designed scalar reward. However, a learned reward model can assign high reward to unwanted behavior. A policy can exploit these errors during training. Later requirements can also identify additional regions where the deployed reward should change. We introduce \emph{flywheel-guided patching}, a mechanism for making such localized corrections without retraining the learned evaluator. An Alignment Flywheel identifies overgeneralized or constraint-violating regions and compiles verified findings into suppression patches. We instantiate the mechanism for Interactionless Inverse Reinforcement Learning using MoE-GUIDE, placing patches in the model's latent space and retrieving only a small local subset during online reward evaluation. In the main MuJoCo benchmark, we evaluate patching without expert-state reset warm starts. We also test whether operator-specified norms absent from the demonstrations can be compiled into the same reward interface and compare this with enforcing the corresponding rules online. The results show that localized corrective information can modify an existing learned reward without providing a complete replacement reward. Compilation can reuse judgments that are expensive or unavailable during control. Cheap deterministic rules may be simpler to check directly at each step. Code for MoE-GUIDE, Alignment Flywheel, and flywheel patching is at https://github.com/decide-ugent/Alignment-Flywheel.