GrowSpace: Learning How to Shape Plants

Yasmeen Hitti, Ionelia Buzatu, Manuel Del Verme, Mark Lefsrud, Florian Golemo, Audrey Durand

Keywords: Reinforcement Learning, Benchmark, Control, Multi-task learning, Fairness, Multi-Objective Learning

Abstract: Plants 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. 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.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2110.08307/code)