Emotional temporal difference <i>Q</i> ‐learning signals in multi‐agent system cooperation: real case studies
Bibliographic record
Abstract
Chaotic non‐linear dynamics approach is now the most powerful tool for scientists to deal with complexities in real cases; and artificial neural networks and neuro‐fuzzy models are widely used for their capabilities in non‐linear modelling of chaotic systems. Chaos, uncertain behaviours, demanding fluctuation, complexity of the traffic flow situations and the problems with those methods, however, caused the forecasting traffic flow values to lack robustness and precision. In this study, the traffic flow forecasting is analysed by emotional concepts and multi‐agent systems (MASs) points of view as a new method. Its architecture is based on a temporal difference (TD) Q ‐learning with a neuro‐fuzzy structure. The performance of TD Q ‐learning method is improved by emotional learning. The concept of emotional TD Q ‐learning method is discussed for the first time in this study. The forecasting algorithm which uses the Q ‐learning algorithm is capable of finding the optimal forecasting approach as the one obtained by the reinforcement learning. In addition, in order to study in a more practical situation, the neuro‐fuzzy behaviours can be modelled by MAS. The real traffic flow signals used for fitting the proposed methods are obtained from interstate I‐494 in Minnesota City in USA and the E17 motorway Gent–Antwerp in Belgium.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".