Apprentissage de la coordination multiagent : Q-learning par jeu adaptatif
Bibliographic record
Abstract
Resume : Dans le cadre de l’apprentissage multiagent, de nombreux travaux ont cherche jusqu’a present a etablir des algorithmes convergents vers un equilibre de Nash en jeux stochastiques. De tels algorithmes sont cependant limites dans la mesure ou ils sont incapables de gerer la multiplicite des equilibres de Nash et de converger vers l’equilibre Pareto-optimal si celui-ci existe. Ces algorithmes utilisent generalement une convention pour la selection de l’equilibre de Nash le plus approprie en cas d’equilibres multiples. Pour palier a cela, nous proposons un algorithme d’apprentissage etendant le Q-learning aux jeux stochastiques non-cooperatifs, qui converge en jeux uniformes (en anglais “self-play”, ce sont des jeux ou tous les agents utilisent le meme algorithme d’apprentissage) vers l’equilibre de Nash Pareto-optimal. Nous presentons des resultats experimentaux montrant la convergence d’un tel algorithme en jeux homogenes vers un equilibre de Nash, en tant qu’equilibre de meilleure reponse mutuelle (donc vers un equilibre de Nash Pareto-optimal), sans besoin de convention de coordination explicite.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".