Synchronization Error Elimination for Heterogeneous Discrete-Time Multi-Agent Systems: A Reinforcement Learning Design Approach
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Bibliographic record
Abstract
This paper proposes a novel reinforcement learning approach to solve the optimal output synchronization problem for discrete-time heterogeneous multi-agent systems. Different from existing learning methods, the optimal control protocol is obtained to guarantee zero synchronization error by solving the augmented algebraic Riccati equations (AREs). The proposed adaptive dynamic programming (ADP) method can stabilize the output synchronization error and solve the output regulator equations implicitly. To eliminate the dependency on information of system dynamics, an online Q-function-based policy iteration (PI) algorithm is developed. Finally, a numerical example is provided to demonstrate the advantages of the proposed ADP over traditional ADP in terms of synchronization performance.
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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 | 0.001 | 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 | 0.001 | 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 it