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Found 4 papers, 0 papers with code
Task-Oriented Koopman-Based Control with Contrastive Encoder
We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop.
Stability Bounds for Learning-Based Adaptive Control of Discrete-Time Multi-Dimensional Stochastic Linear Systems with Input Constraints
We consider the problem of adaptive stabilization for discrete-time, multi-dimensional linear systems with bounded control input constraints and unbounded stochastic disturbances, where the parameters of the true system are unknown.
Learning-Based Adaptive Control for Stochastic Linear Systems with Input Constraints
We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i. i. d.
MBB: Model-Based Baseline for Global Guidance of Model-Free Reinforcement Learning via Lower-Dimensional Solutions
On the other end, "classical methods" such as optimal control generate solutions without collecting data, but assume that an accurate model of the system and environment is known and are mostly limited to problems with low-dimensional (lo-dim) state spaces.
