Interpretable online scheduling for chemical batch plants with attention augmented reinforcement learning agents
Bibliographic record
Abstract
A method for training Deep Reinforcement Learning agents with self-attention modules is presented to address online scheduling decisions in batch chemical plants. The agent is designed to generate multiple decisions at the same time in a partially observable environment; these decisions can be either discrete or continuous. The use of self-attention modules is justified by their increasing popularity in Natural Language Processing (NLP) methods over Recurrent Neural Networks (RNNs). This method leverages the attention-based models by using the attention matrices generated by the agent to produce a numeric interpretation of the agent’s logic. Additionally, a second attention matrix with fixed parameters was embedded in the architecture to define the relevance of each part of the environment. The information from these matrices is demonstrated to be useful for interpreting the relevance of key aspects in the environment and to track the change of the agent’s focal points in non-stationary environments. The methodology is tested in two batch chemical plant case studies, demonstrating the advantages and limitations in comparison to an agent built with RNNs. The interpretability of the decisions and the logic of the agent showed an option to approach the so called black-box characteristic of data-driven models.
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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".