Farm-gym: A modular reinforcement learning platform for stochastic agronomic games

Odalric-Ambrym Maillard, Timothée Mathieu, Debabrota Basu

Keywords: Reinforcement Learning, Agronomy, Software, gym environment, Farming

TL;DR: We introduce Farm-gym, an open-source farming environment written in Python, that models sequential decision-making in farms in order showcase new challenges to the RL community, and stimulate collaboration with agronomy community.

Abstract:

We introduce Farm-gym, an open-source farming environment written in Python, that models sequential decision-making in farms using Reinforcement Learning (RL). Farm-gym conceptualizes a farm as a dynamical system with many interacting entities. Leveraging a modular design, it enables us to instantiate from very simple to highly complicated environments.
Contrasting many available gym environments, Farm-gym features intrinsically stochastic games, using stochastic growth models and weather data. Further, it enables to create farm games in a modular way, activating or not the entities (e.g. weeds, pests, pollinators), and yielding non-trivial coupled dynamics. Finally, every game can be customized with .yaml files for rewards, feasible actions, and initial/end-game conditions. We illustrate some interesting features on simple farms. We also showcase the challenges posed by Farm-gym to the deep RL algorithms, in order to stimulate studies in the RL community.