Recurrent Reinforcement Learning Strategy with a Parameterized Agent for Online Scheduling of a State Task Network Under Uncertainty
Bibliographic record
Abstract
This study presents a framework for developing reinforcement learning hybrid agents that can build online schedules for state task networks under epistemic and aleatoric uncertainty. The hybrid agent can perform multiple discrete or continuous decisions at every time interval. To approach the uncertainty in the scheduling process, the hybrid agent is augmented with a set of LSTM layers that integrate a sequence of observations. This feature allows for the consideration of previous information to make decisions in view of the realization and propagation of uncertainties throughout the plant. Moreover, the techniques required for an efficient training oriented toward the objective function are described. The method is implemented in two case studies for validation and testing of the agent subject to epistemic and aleatoric uncertainty. A similar hybrid agent without recurrence is used as a benchmark. The proposed hybrid agent accumulated larger rewards while minimizing the number of constraint violations in the process under uncertainty, thus, making this online scheduling agent attractive for industrial-scale applications.
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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 | 0.002 |
| 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 | 0.001 |
| 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".