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This paper investigates the capability of embodied agents to perform a sequential counting task. Drawing inspiration from honeybee studies, we present a minimal numerical cognition task wherein an agent navigates a 1D world marked with landmarks to locate a previously encountered food source. We evolved embodied artificial agents controlled by dynamical recurrent neural networks to be capable of associating a food reward with encountering a number of landmarks sequentially. To eliminate the possibility of the evolved agents relying on distance to locate the target landmark, we varied the positions of the landmarks across trials. Our experiments demonstrate that embodied agents equipped with relatively small neural networks can accurately enumerate and remember up to five landmarks when encountered sequentially. Counter to the intuitive notion that numerical cognition is a complex, higher cortical function, our findings support the idea that numerical discrimination can be achieved in relatively compact neural circuits.