Reinforcement Learning-based Control of a Buck Converter: A Comparative Study of DQN and DDPG Algorithms
Bibliographic record
Abstract
Reinforcement learning (RL) has emerged as a promising approach for controlling power electronics systems due to its ability to control nonlinear systems without accurate models, and handle unknown changes. This paper presents a comparative study of two widely used deep RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN), for controlling the output voltage of DC/DC buck converters. DDPG and DQN are tailored to handle continuous and discrete environments, respectively. The performance of these algorithms is evaluated through simulations with steady-state error, overshoot, settling time, and training time serving as evaluation metrics. The simulation results demonstrate that both DDPG and DQN show excellent performance in controlling power converters, with DDPG generally performing better than DQN in systems with unpredictable changes.
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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".