Data-Efficient MADDPG Based on Self-Attention for IoT Energy Management Systems
Bibliographic record
Abstract
In this study, the simulated real-world Demand Response (DR) potential is controlled and optimized when household load characteristics are analyzed based on historical data information. To determine the optimal DR potential in smart homes integrated with IoT energy management systems, a multi-agent reinforcement learning framework can be one of the best solutions to handle various household appliances’ control activities associated with stochastic nature. However, the main problem with multi-agent systems is a nonstationary environment that is arisen by the agents. Consequently, this can cause more system uncertainties. Hence, it requires an excessive number of interactions with the environment for training which leads to a data inefficient reinforcement learning model. Thus, we propose a new approach using a Multi-Agent Deep Deterministic Policy Gradient based on Bi-directional Long Short Term Memory and Attention Mechanism (BiLSTMA-MADDPG) to extract more useful information. Therefore, we developed an improved MADDPG model that exploits the BiLSTM layer to store a history of experience in the MADDPG’s replay buffer, and the Attention Mechanism to reduce the model dependency upon the number of samples since it can extract the most valuable data and ignore the less important ones. In this way, BiLSTMA-MADDPG can perform better than the conventional MADDPG even with the small sample environment to motivate the exploration of a more robust and data-efficient regime. Therefore, the attention mechanism enables MADDPG to be more effective and scalable in learning in complex real-world multi-agent environments. Simulation results are obtained for a household environment with three cooperated agents to control the following devices, washing machine, air conditioner, and electric vehicle. The model performance is validated, showing an improvement to the data efficiency and convergence speed, and a promise for a real-life application in terms of appliance energy consumption.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".