Reducing the complexity of multiagent reinforcement learning
Bibliographic record
Abstract
It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment's states. It was shown, however, that the learning complexity for the goal-directed problems may be substantially reduced by initializing the Q-values with a "good" approximative function. In the multiagent case, there exists such a good approximation for a big class of problems, namely, for goal-directed stochastic games. These games, for example, can reflect coordination and common interest problems of cooperative robotics. The approximative function for these games is nothing but the relaxed, single-agent, problem solution, which can easily be found by each agent individually. In this article, we show that (1) an optimal single-agent solution is a "good" approximation for the goal-directed stochastic games with action-penalty representation and (b) the complexity is reduced when the learning is initialized with this approximative function, as compared to the uninformed case.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".