Hybrid Deep Reinforcement Learning Agent for Online Scheduling and Control for Chemical Batch Plants
Bibliographic record
Abstract
This study presents a framework for the implementation of a Deep Reinforcement Learning (DRL) agent for optimal scheduling and control integration on flow-shop batch plants with input variability. The agent is designed to take multiple decisions at every time interval which allows for the integration of scheduling and control. A hybrid agent with multiple decision outputs is used to perform online scheduling and control. To account for the short-term history of the process, the agent approaches the optimization problem as a Partially Observable Markov Decision Process (POMDP). The agent makes use of a set of Long Short-Term Memory cells (LSTM) to correlate sequential states from the environment to be aware of its evolution when taking decisions. To demonstrate the advantages and limitations of the hybrid agent, the method is implemented on a batch plant under variability in the inputs. Results showed that the agent’s policy reacted to the fluctuations in concentration from raw materials. To validate the proposed method, a comparison with an agent trained on an environment with fixed inputs was performed to demonstrate the adaptive behavior of the agent developed with the presented framework.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".