Reducing the complexity of multiagent reinforcement learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it