Farm-gym: A modular reinforcement learning platform for stochastic agronomic games - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

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

Odalric-Ambrym Maillard
Timothée Mathieu
Debabrota Basu

Résumé

We introduce Farm-gym, an open-source farming environment written in Python, that models sequential decisionmaking 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.
Fichier principal
Vignette du fichier
2022_AAAI_AIAFS (2).pdf (4.47 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03960683 , version 1 (27-01-2023)

Identifiants

  • HAL Id : hal-03960683 , version 1

Citer

Odalric-Ambrym Maillard, Timothée Mathieu, Debabrota Basu. Farm-gym: A modular reinforcement learning platform for stochastic agronomic games. AIAFS 2023 - Artificial Intelligence for Agriculture and Food Systems, Feb 2023, Wahington DC, United States. ⟨hal-03960683⟩
101 Consultations
89 Téléchargements

Partager

Gmail Facebook X LinkedIn More