A Practical Reinforcement Learning (RL) Controller Design for Nonlinear Systems
Bibliographic record
Abstract
This paper presents a practically implementable reinforcement learning (RL) approach for process control applications. Standard model-free RL approaches are not applicable in practice because the learning process of an RL agent requires random exploration of state and action spaces, which can compromise process safety and economic objectives. To tackle this issue, an offline training strategy is proposed by leveraging existing model predictive control (MPC) to pre-train the RL agent. MPC actions, calculated offline by solving the MPC optimization problem based on a wide range of initial conditions, along with its objective function are utilized to pretrain the actor and critics of the RL agent. The pre-trained RL controller, with similar performance to the MPC performance, is then utilized for online control for further training. The efficacy of the proposed RL controller to improve the tracking performance is demonstrated using a simulation example for a pH neutralization process.
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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".