A survey and comparative evaluation of actor‐critic methods in process control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Actor‐critic (AC) methods have emerged as an important class of reinforcement learning (RL) paradigm that enables model‐free control by acting on a process and learning from the consequence. To that end, these methods utilize artificial neural networks, which are synergized for action evaluation and optimal action prediction. This feature is highly desirable for process control, especially when the knowledge about a process is limited or when it is susceptible to uncertainties. In this work, we summarize important concepts of AC methods and survey their process control applications. This treatment is followed by a comparative evaluation of the set‐point tracking and robustness of controllers based on five prominent AC methods, namely, DDPG, TD3, SAC, PPO, and TRPO, in five case studies of varying process nonlinearity. The training demands and control performances indicate the superiority of DDPG and TD3 methods, which rely on off‐policy, deterministic search for optimal action policies. Overall, the knowledge base and results of this work are expected to serve practitioners in their efforts toward further development of autonomous process control strategies.
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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 | 0.003 | 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 | 0.000 | 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 it