Sarsa-based Model Predictive Control with Improved Performance and Computational Complexity
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Bibliographic record
Abstract
This paper investigates Sarsa-based model predictive control (MPC) for constrained discrete-time linear systems with mixed uncertainties, including parametric uncertainties and external additive disturbances. With a system subject to parametric uncertainties, i.e., an inaccurate system model in hand, the Sarsa-based update policy is constructed for the intention of preserving the optimality brought by MPC. A linear state feedback control policy via MPC is applied to achieve the regulation objective. By incorporating these two techniques into the standard tube MPC framework, the Sarsa-based MPC which essentially is a data-driven reinforcement learning-based method is proposed. The MPC parameters which are subject to the Sarsa-based update policy are specified. They include the approximated model parameters, a linear feedback control gain, and an auxiliary disturbance set used to enhance the robustness of tube MPC. The proposed method is computational inexpensive and robust. This is validated by a numerical example.
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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 it