Learning crop management by reinforcement: ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Learning crop management by reinforcement: gym-DSSAT
Author(s) :
Gautron, Romain [Auteur]
International Center for Tropical Agriculture [Colombie] [CIAT]
Scool [Scool]
Padrón, Emilio [Auteur]
University of A Coruña [UDC]
Preux, Philippe [Auteur]
Scool [Scool]
Bigot, Julien [Auteur]
Maison de la Simulation [MDLS]
Maillard, Odalric Ambrym [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Scool [Scool]
Hoogenboom, Gerrit [Auteur]
University of Florida [Gainesville] [UF]
Teigny, Julien [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Scool [Scool]
International Center for Tropical Agriculture [Colombie] [CIAT]
Scool [Scool]
Padrón, Emilio [Auteur]
University of A Coruña [UDC]
Preux, Philippe [Auteur]

Scool [Scool]
Bigot, Julien [Auteur]
Maison de la Simulation [MDLS]
Maillard, Odalric Ambrym [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Scool [Scool]
Hoogenboom, Gerrit [Auteur]
University of Florida [Gainesville] [UF]
Teigny, Julien [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Scool [Scool]
Conference title :
AIAFS 2023 - 2nd AAAI Workshop on AI for Agriculture and Food Systems
City :
Washignton DC
Country :
Etats-Unis d'Amérique
Start date of the conference :
2023-02-13
English keyword(s) :
Reinforcement learning
Agronomy
Agronomy
HAL domain(s) :
Informatique [cs]/Intelligence artificielle [cs.AI]
English abstract : [en]
We introduce gym-DSSAT, a gym environment for crop management tasks, that is easy to use for training Reinforcement Learning (RL) agents. gym-DSSAT is based on DSSAT, a state-of-the-art mechanistic crop growth simulator. ...
Show more >We introduce gym-DSSAT, a gym environment for crop management tasks, that is easy to use for training Reinforcement Learning (RL) agents. gym-DSSAT is based on DSSAT, a state-of-the-art mechanistic crop growth simulator. We modify DSSAT so that an external software agent can interact with it to control the actions performed in a crop field during a growing season. The RL environment provides predefined decision problems without having to manipulate the complex crop simulator. We report encouraging preliminary results on a use case of nitrogen fertilization for maize. This work opens up opportunities to explore new sustainable crop management strategies with RL, and provides RL researchers with an original set of challenging tasks to investigate.Show less >
Show more >We introduce gym-DSSAT, a gym environment for crop management tasks, that is easy to use for training Reinforcement Learning (RL) agents. gym-DSSAT is based on DSSAT, a state-of-the-art mechanistic crop growth simulator. We modify DSSAT so that an external software agent can interact with it to control the actions performed in a crop field during a growing season. The RL environment provides predefined decision problems without having to manipulate the complex crop simulator. We report encouraging preliminary results on a use case of nitrogen fertilization for maize. This work opens up opportunities to explore new sustainable crop management strategies with RL, and provides RL researchers with an original set of challenging tasks to investigate.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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