NeuraFlux: A scalable and adaptive framework for autonomous data-driven multi-agent power optimization
Bibliographic record
Abstract
NeuraFlux is an open-source, adaptive multi-agent reinforcement learning platform designed to optimize energy management in complex, dynamic environments. It addresses key challenges in coordinating distributed energy resources, including scalability limitations, difficulties in managing competing objectives, and lack of real-time adaptability. This paper presents two primary contributions: the theoretical foundations of NeuraFlux and its significance in modern power systems infrastructure and control, along with a novel training algorithm optimized for real-world deployment performance. Through three case studies—energy storage market arbitrage, heating, ventilation, and air conditioning (HVAC) system control, and electric vehicle grid integration—NeuraFlux’s effectiveness in managing intricate, multi-agent, and multi-objective optimization challenges is demonstrated. The modularity and scalability demonstrated in these examples, combined with the framework’s technical robustness for edge deployment, establish NeuraFlux as a powerful and practical tool for deploying advanced control systems in modern power and energy systems. • Scalable Optimization: Multi-agent deep reinforcement learning-based framework for optimizing distributed energy resources, aimed at supporting real-time, data-driven power systems optimization in diverse scenarios. • Modular and Scalable Design: The framework’s architecture is both modular and scalable, integrating with various assets and consumer devices, and featuring a novel training algorithm optimized for real-world performance. • Open Source Contribution: By releasing NeuraFlux as open-source, the research promotes transparency, collaboration, and innovation, enabling diverse applications and advancements in energy optimization contexts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".