  Plants are dynamic systems that are integral to our existence and survival.
Plants face environment changes and adapt over time to their surrounding
conditions. We argue that plant responses to an environmental stimulus are a
good example of a real-world problem that can be approached within a
reinforcement learning (RL)framework. With the objective of controlling a plant
by moving the light source, we propose GrowSpace, as a new RL benchmark. The
back-end of the simulator is implemented using the Space Colonisation
Algorithm, a plant growing model based on competition for space. Compared to
video game RL environments, this simulator addresses a real-world problem and
serves as a test bed to visualize plant growth and movement in a faster way
than physical experiments. GrowSpace is composed of a suite of challenges that
tackle several problems such as control, multi-stage learning,fairness and
multi-objective learning. We provide agent baselines alongside case studies to
demonstrate the difficulty of the proposed benchmark.
