Bridging control and reinforcement learning with partial model knowledge
Bibliographic record
Abstract
This thesis develops a series of complementary approaches that bridge control theory and reinforcement learning through systematic exploitation of partial model knowledge. Control theory leverages known system structure to deliver precise solutions but struggles with unknown dynamics, whereas reinforcement learning is flexible yet typically suffers from poor sample efficiency. The proposed methods integrate the strengths of both paradigms by combining model-based control where knowledge is available with learning-based adaptation for unknown components. We first consider linear systems and introduce Partial Knowledge Least Squares Policy Iteration (PLSPI), which decomposes system dynamics into known and unknown components (A = A1 + A2, B = B1 + B2). This formulation enables a principled interpolation between optimal control and reinforcement learning, improving sample efficiency while retaining robustness to modeling errors. We then provide a theoretical analysis explaining when and why PLSPI achieves superior convergence compared to standard LSPI. Through spectral analysis of the value function estimator, we show that the estimator norm in PLSPI can be smaller under certain conditions, leading to reduced variance and improved convergence behavior. Experiments across different partial knowledge configurations further illustrate how the design of known model components influences learning performance. We extend the partial knowledge paradigm to nonlinear systems through a hybrid architecture that combines structured control modules with neural network policies. Different from the PLSPI structure, this framework explicitly separates the roles of known and unknown dynamics within the policy, enabling effective nonlinear control with improved sample efficiency compared to black-box deep reinforcement learning. Finally, we develop DiLQR, a framework that makes the iterative Linear Quadratic Regulator (iLQR), a numerical nonlinear controller, fully differentiable via implicit differentiation. The proposed method computes exact gradients while accounting for all parameter dependencies, and introduces a forward algorithm with O(T) complexity, yielding substantial gains in computational efficiency and learning performance. Overall, this thesis presents principled methods for leveraging structural knowledge without sacrificing adaptability, with applications in robotics, autonomous systems, and industrial process control where sample efficiency and reliability are critical.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".