A Near-Optimal Solution to a Class of Deep Structured Teams with Nonlinear Dynamics
Bibliographic record
Abstract
Classical control offers reliable, optimal control strategies in multi-agent settings to sequential decision-making problems. However, these methods usually rely on strong assumptions on the system dynamics. In contrast, model-free control provides the flexibility to deal with unmodeled system dynamics to perform complex tasks. In this work, we study the connection between the two methods in multi-agent systems. We focus on deep structured teams where the agent’s evolution is a known linear function of each agent’s state and the linear regression of all agents’ states and actions plus an unknown nonlinear term with a bounded Lipschitz constant. Furthermore, the cost is considered to be quadratic for the states and actions of all the agents. We prove the existence of a near-optimal solution in the convex vicinity of initialized controllers obtained from model-based LQR methods. We show these initialized control strategies are derived by solving an Algebraic Riccati Equation (ARE), obtained by neglecting the nonlinear terms. Finally, we provide convergence guarantees to the optimal solution using a derivative-free policy gradient approach. Simulations confirm the validity of the analytical results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".