6 papers
Some Essential Constructive Foundations for Systems and Control
Pavel Osinenko
This work develops several constructive foundations for systems and control within Bishop-style constructive mathematics. For an engineer, the guiding principle is that an object c…
An Agency-Transferring Model-Free Policy Enhancement Technique
Anton Bolychev, Georgiy Malaniya, Sinan Ibrahim +1
Training reinforcement learning (RL) policies from scratch is costly: it requires careful reward and environment design, extensive tuning, and substantial computation. Yet many con…
Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies
Sinan Ibrahim, Grégoire Ouerdane, Hadi Salloum +3
The objective comparison of Reinforcement Learning (RL) algorithms is notoriously complex as outcomes and benchmarking of performances of different RL approaches are critically sen…
Towards a constructive framework for control theory
Pavel Osinenko
This work presents a framework for control theory based on constructive analysis to account for discrepancy between mathematical results and their implementation in a computer, als…
Some remarks on robustness of sample-and-hold stabilization
Patrick Schmidt, Pavel Osinenko, Stefan Streif
This work studies robustness to system disturbance and measurement noise of some popular general practical stabilization techniques, namely, Dini aiming, optimization-based stabili…
Some remarks on practical stabilization via CLF-based control under measurement noise
Patrick Schmidt, Pavel Osinenko, Stefan Streif
Practical stabilization of input-affine systems in the presence of measurement errors and input constraints is considered in this brief note. Assuming that a Lyapunov function and…