A Review of Recent Advances on Reinforcement Learning for Smart Home Energy Management
Bibliographic record
Abstract
Smart home energy management is one of the core problems in modern power grids. With the increasing adoption of different types of electric appliances and on-site intermittent renewable energy generation, it has been very challenging to use conventional control techniques for such energy management problems. Reinforcement Learning (RL) has attracted growing research interest recently; it also demonstrates its great potential to enhance smart home performance while addressing some limitations of other advanced control techniques, such as model predictive control. In this paper, we present a review of the recent advances on RL for smart home energy management. The problem of smart home energy management, the background for RL algorithms, and the survey of recent advances on RL for the smart home are presented. However, even though RL-based smart home controls have gained increasing research interest, it is in the beginning research stage. Several questions in this field are still not well-studied and worth further investigation, including data-efficient reinforcement learning, safety concerns, and how to include human behaviors in the loop of making control decisions. In this short survey, we also discuss the challenges and potential opportunities using RL in smart home control.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".