Multi-Agent Deep Reinforcement Learning Based Adaptive Control for Smart Greenhouse Integrated Microgrid
Bibliographic record
Abstract
This paper proposes a multi-agent deep reinforcement learning (MADRL) framework for adaptive control of a smart greenhouse integrated into a renewable-based microgrid. The system jointly regulates temperature, humidity, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CO}_{2}$</tex> concentration, lighting, water pumping, and battery storage to reduce the electric grid dependence. To enhance training efficiency, the actor networks of Twin Delayed Deep Deterministic Policy Gradient (TD3) agents are pretrained using imitation learning on datasets generated from nonlinear model predictive control (NMPC). Simulation results across multiple tomato growth stages show that MADRL achieves performance comparable to NMPC in microclimate and energy regulation while operating almost an order of magnitude faster in online computation. Moreover, under uncertain weather forecasts with noise and bias, MADRL outperforms NMPC in both tracking accuracy and energy efficiency, demonstrating strong robustness to forecast errors and adaptability to uncertainty. These findings highlight the potential of MADRL as a computationally efficient and resilient alternative for greenhouse-microgrid operation, paving the way for real-time applications and future extensions to profit-driven, year-round control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".