GrowSpace: A reinforcement learning environment for plant architecture

Abstract

Plants adapt over time to their surrounding conditions. We argue that no current plant simulator addresses plant responses to an environmental stimulus over time through gamification and sequential decision making. We propose the GrowSpace simulator that is built within a reinforcement learning framework, providing a sandbox environment to test out hypotheses. GrowSpace currently focuses on the behavioral response of plants to light displacement, with the objective of controlling a plant’s shape by moving a light source. The back-end of the simulator is implemented using the Space Colonisation Algorithm. This simulator serves as a low-dimensional test bed to visualize plant growth and movement with respect to a light stimulus and to better understand a plant’s reactions to these conditions. GrowSpace provides an artificial modular environment that is simulated to the advancements of plant biological systems all while using a reinforcement learning framework. Tasks are crafted and tested with artificial intelligence algorithms to further understand the concept of plant growth control through light. We equally demonstrate that sequential control of plant branching is hard challenge for the plant science community as well as the reinforcement learning community.