Multiadvisor Reinforcement Learning for Multiagent Multiobjective Smart Home Energy Control
Bibliographic record
Abstract
Effective automated smart home energy control is essential for smart grid approaches to demand response (DR). This is a multiobjective adaptive control problem because it balances an appliance’s primary objective with demand response objectives. One challenge comes from the heterogeneous nature of objectives, requiring tradeoffs between comfort, cost, and other objectives. Another challenge comes from the heterogeneous dynamics, which result from different environments and the different appliances used. Another challenge is nonstationary nature of dynamics and rewards due to seasonal changes and time-varying user preferences. Finally, we consider computational challenges, required by the real-time aspect of the control problem, particularly notable due to “the curse of dimensionality.” We propose a multiagent multiadvisor reinforcement learning framework to address these challenges. We design a smart-home simulation to demonstrate the performance (in terms of weighted reward) of our approach relative to competitive single-objective reinforcement learning algorithms. Furthermore, we theoretically and empirically demonstrate the linear computational scalability of the algorithm. Finally, we identify the need for key performance measures of the proposed system by considering the effect of selected preferences on agents. Overall, the proposed algorithm is reasonably competitive with conventional approaches while simultaneously enabling behavior changes with change in preferences without requiring more data.
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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 | 0.002 | 0.003 |
| 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".