Decentralized Q-Learning with Constant Aspirations in Stochastic Games
Bibliographic record
Abstract
In decentralized stochastic control, coordination among control agents is typically required in order to achieve acceptable system performance. In practice, pertinent information about the system-in the form of the cost function, state transition probabilities, and past actions of other agents-is often unavailable to some or all agents, and this serves as an obstacle to finding optimal control policies. In this paper, a decentralized control problem is modelled as a stochastic game in which (i) the specific game being played is unknown, and (ii) players never observe the actions used by other agents. This information structure exacerbates the already difficult challenge of decentralized policy evaluation in stochastic games. This paper presents a two-timescale reinforcement learning algorithm for stochastic games, in which players engage in decentralized policy evaluation during the finer timescale and update their baseline policies in the coarser timescale. The algorithm presented here comes with provable convergence guarantees and uses only local information, in the form of local cost readings, the history of local actions, and the state information.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".