A survey and comparative evaluation of actor‐critic methods in process control
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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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".