Integral reinforcement learning solutions for a synchronisation system with constrained policies
Bibliographic record
Abstract
A class of the differential games is considered where the agents employ constrained control strategies, and the mutual interactions between the agents are restricted by an undirected graph topology. The dynamical behaviour of the agents and the applied control policies are evaluated using local non‐linear performance indices. The solution of the differential game is obtained via a game‐theoretic mathematical framework based on adaptive integral reinforcement learning (IRL) schemes. The constrained optimality conditions for the graphical game are found using Bellman's optimality principles. It is demonstrated that, solving the game's coupled IRL‐Bellman optimality equations with constrained control policies yields a Nash equilibrium solution. Online adaptive learning solutions are developed using value iteration processes and means of the adaptive critics. Neural network structures are adopted to approximate the constrained optimal control strategies and the respective optimal value functions for each agent in a distributed fashion. The robustness of the proposed solutions is tested using uncertain dynamical learning environment and graph with large time‐varying deviations in the connectivity weights.
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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".