An Enhanced Adaptivity of Reinforcement Learning-Based Temperature Control in Buildings Using Generalized Training
Bibliographic record
Abstract
In this paper, we present an adaptive Reinforcement Learning (RL) agent training approach which aims to provide a temperature control adaptable to various types of buildings. The main purpose of the proposed method is to avoid repeating the training of RL agents on every new building and therefore skip the modeling part and ease the spread of RL-based controllers. This study includes analysis of the proposed method working along with ACKTR, a state of the art RL algorithm, regarding parameter tuning, algorithm convergence, consumption reduction and state observation accuracy. The RL based controller is applied first to single rooms and then extended to entire buildings, to demonstrate its generalization efficiency. To measure the performance of the RL controller, comparisons with MPC and ON/OFF based controllers are performed. On each test, the adaptive RL temperature control was similar to MPC and better than ON/OFF control, while being able to reach up to 5% of energy savings compared to MPC and 10% compared to ON/OFF 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".