# Learning Expected Reward for Switched Linear Control Systems: A Non-Asymptotic View

arXiv: https://arxiv.org/abs/2006.08105 · HTML: https://mlsanity.com/paper/2006.08105/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space](https://mlsanity.com/paper/2205.12448/) (2022)

## Abstract (arXiv)

In this work, we show existence of invariant ergodic measure for switched linear dynamical systems (SLDSs) under a norm-stability assumption of system dynamics in some unbounded subset of ℝn. Consequently, given a stationary Markov control policy, we derive non-asymptotic bounds for learning expected reward (w.r.t the invariant ergodic measure our closed-loop system mixes to) from time-averages using Birkhoff's Ergodic Theorem. The presented results provide a foundation for deriving non-asymptotic analysis for average reward-based optimal control of SLDSs. Finally, we illustrate the presented theoretical results in two case-studies.