4 research outputs found
Learning Crop Management by Reinforcement: gym-DSSAT
International audienceWe 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
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study
Background Weight loss trajectories after bariatric surgery vary widely
between individuals, and predicting weight loss before the operation remains
challenging. We aimed to develop a model using machine learning to provide
individual preoperative prediction of 5-year weight loss trajectories after
surgery. Methods In this multinational retrospective observational study we
enrolled adult participants (aged 18 years) from ten prospective cohorts
(including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese
Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse
Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and
SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year
followup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band.
Patients with a previous history of bariatric surgery or large delays between
scheduled and actual visits were excluded. The training cohort comprised
patients from two centres in France (ABOS and BAREVAL). The primary outcome was
BMI at 5 years. A model was developed using least absolute shrinkage and
selection operator to select variables and the classification and regression
trees algorithm to build interpretable regression trees. The performances of
the model were assessed through the median absolute deviation (MAD) and root
mean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in
ten countries were included in the analysis, corresponding to 30 602
patient-years. Among participants in all 12 cohorts, 7701 (753%) were
female, 2530 (247%) were male. Among 434 baseline attributes available
in the training cohort, seven variables were selected: height, weight,
intervention type, age, diabetes status, diabetes duration, and smoking status.
At 5 years, across external testing cohorts the overall mean MAD BMI was
28 kg/m (95% CI 26-30) and mean RMSE BMI was
47 kg/m (44-50), and the mean difference
between predicted and observed BMI was-03 kg/m (SD 47).
This model is incorporated in an easy to use and interpretable web-based
prediction tool to help inform clinical decision before surgery.
InterpretationWe developed a machine learning-based model, which is
internationally validated, for predicting individual 5-year weight loss
trajectories after three common bariatric interventions.Comment: The Lancet Digital Health, 202
Learning crop management by reinforcement: gym-DSSAT
International audienceWe 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
Learning Crop Management by Reinforcement: gym-DSSAT
International audienceWe 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