Searcher-Shoot: a Reinforcement Learning approach to understand climbing plant behaviour

Plants’ structure is the result of constant evolution towards the adaptation to the surrounding environment. From this perspective, our goal is to investigate the mass and radius distribution of a peculiar plant organ, namely the searcher shoot, by providing a Reinforcement Learning (RL) environment, that we call Searcher-Shoot, which considers the mechanics due to the mass of the shoot and leaves. We uphold the theory that plants can maximize their length, avoiding a maximal stress threshold. To do this, we explore whether the mass distribution along the stem is efficient, formulating this hypothesis as a Markov Decision Process (MDP). By exploiting this strategy, we are able to mimic and thus understand the plant’s behavior, finding that shoots decrease their diameters smoothly in order to efficiently distribute the mass. The strong agreement between our results and the experimental data allows us to remark on the strength of our approach in the analysis of biological systems traits.