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Found 16 papers, 0 papers with code
Thompson Sampling in Partially Observable Contextual Bandits
Accordingly, a fundamental problem is that of balancing exploration (i. e., pulling different arms to learn their parameters), versus exploitation (i. e., pulling the best arms to gain reward).
Thompson Sampling Efficiently Learns to Control Diffusion Processes
To the best of our knowledge, this is the first such result for Thompson sampling in a diffusion process control problem.
Regret Analysis of Certainty Equivalence Policies in Continuous-Time Linear-Quadratic Systems
This work theoretically studies a ubiquitous reinforcement learning policy for controlling the canonical model of continuous-time stochastic linear-quadratic systems.
Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations
Contextual bandits are canonical models for sequential decision-making under uncertainty in environments with time-varying components.
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts
Contextual bandits are widely-used in the study of learning-based control policies for finite action spaces.
Joint Learning-Based Stabilization of Multiple Unknown Linear Systems
Learning-based control of linear systems received a lot of attentions recently.
Bayesian Algorithms Learn to Stabilize Unknown Continuous-Time Systems
Linear dynamical systems are canonical models for learning-based control of plants with uncertain dynamics.
Joint Learning of Linear Time-Invariant Dynamical Systems
Linear time-invariant systems are very popular models in system theory and applications.
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits
We propose a Thompson Sampling algorithm for partially observable contextual multi-armed bandits, and establish theoretical performance guarantees.
Reinforcement Learning Policies in Continuous-Time Linear Systems
Linear dynamical systems that obey stochastic differential equations are canonical models.
Online Distributed Estimation of Principal Eigenspaces
Principal components analysis (PCA) is a widely used dimension reduction technique with an extensive range of applications.
Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems
We provide numerical analyses for the performance of two methods: stochastic feedback, and stochastic parameter.
On Applications of Bootstrap in Continuous Space Reinforcement Learning
In decision making problems for continuous state and action spaces, linear dynamical models are widely employed.
Input Perturbations for Adaptive Control and Learning
This paper studies adaptive algorithms for simultaneous regulation (i. e., control) and estimation (i. e., learning) of Multiple Input Multiple Output (MIMO) linear dynamical systems.
Finite Time Adaptive Stabilization of LQ Systems
There are only a few existing non-asymptotic results and a full treatment of the problem is not currently available.
Optimism-Based Adaptive Regulation of Linear-Quadratic Systems
The main challenge for adaptive regulation of linear-quadratic systems is the trade-off between identification and control.
