A semi-centralized multi-agent RL framework for efficient irrigation scheduling
Bibliographic record
Abstract
Efficient water management in agriculture is essential for addressing the growing freshwater scarcity crisis. Multi-Agent Reinforcement Learning (MARL) has emerged as a promising method for solving daily irrigation scheduling problems in spatially variable fields, where management zones are employed to account for field variability. To enhance the application of MARL to address daily irrigation scheduling in large-scale fields with significant spatial variation, this study proposes a Semi-Centralized MARL (SCMARL) framework. The SCMARL framework adopts a hierarchical structure, decomposing the daily irrigation scheduling problem into two levels of decision-making. At the top level, a centralized coordinator agent determines irrigation timing, which is modeled as a discrete variable, based on field-wide soil moisture data, crop conditions, and weather forecasts. At the lower level, decentralized local agents use local soil moisture, crop, and weather information to determine the appropriate irrigation amounts for each management zone. To address the issue of non-stationarity in this framework, a state augmentation technique is employed, wherein the coordinator’s decision is incorporated into the decision-making process of the local agents. The SCMARL framework, which leverages the Proximal Policy Optimization algorithm for training the agents, is evaluated on a large-scale field in Lethbridge, Canada, and compared with an existing MARL irrigation scheduling approach. The results demonstrate improved performance, achieving a 4.0% reduction in water use and a 6.3% increase in irrigation water use efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".