Multi-agent Multi-game Entity Transformer: Towards Generalist Models in MARL
Bibliographic record
Abstract
Building large-scale generalist pre-trained models for many tasks is becoming an emerging and potential direction in reinforcement learning (RL).Research such as Gato and Multi-Game Decision Transformer have displayed outstanding performance and generalization capabilities on many games and domains.However, there exists a research blank about developing highly capable and generalist models in multi-agent RL (MARL), which can substantially accelerate progress toward general AI.To fill this gap, we propose Multi-Agent multi-Game ENtity TrAnsformer (MA-GENTA) from the entity perspective as orthogonal research to previous time-sequential modeling.Specifically, to deal with different state/observation spaces in different games, we analogize games as languages by aligning one single game to one single language, thus training different "tokenizers" and a shared transformer for various games.The feature inputs are split according to different entities and tokenized in the same continuous space.Then, two types of transformer-based models are proposed as permutationinvariant architectures to deal with various numbers of entities and capture the attention of different entities.MAGENTA is trained on
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".