Bibliographic record
Abstract
Classically, an approach to the policy learning in multia-gent systems supposed that the agents, via interactions and/or by using preliminary knowledge about the reward functions of all players, would find an interdependent solution called “equilibrium”. Recently, however, certain researchers ques-tion the necessity and the validity of the concept of equilib-rium as the most important multiagent solution concept. They argue that a “good ” learning algorithm is one that is efficient with respect to a certain class of counterparts. Adaptive play-ers is an important class of agents that learn their policies sep-arately from the maintenance of the beliefs about their coun-terparts ’ future actions and make their decisions based on that policy and the current belief. In this paper we propose an ef-ficient learning algorithm in presence of the adaptive coun-terparts called Adaptive Dynamics Learner (ADL) which is able to learn an efficient policy over the opponents ’ adaptive dynamics rather than over the simple actions and beliefs and, by so doing, to exploit this dynamics to obtain a higher util-ity than any equilibrium strategy can provide. We tested our algorithm on a big set of the most known and demonstrative matrix games and observed that ADL agent is highly efficient against Adaptive Play Q-learning (APQ) agent and Infinites-imal Gradient Ascent (IGA) agent. In self-play, when possi-ble, ADL is able to converge to a Pareto optimal strategy that maximizes the welfare of all players instead of an equilibrium strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".