# Setpoint Tracking with Partially Observed Loads

arXiv: https://arxiv.org/abs/1709.04077 · HTML: https://mlsanity.com/paper/1709.04077/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads](https://mlsanity.com/paper/2301.02593/) (2023)
- [Online Dynamic Submodular Optimization](https://mlsanity.com/paper/2306.10835/) (2023)

## Abstract (arXiv)

We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feedback where in each round the aggregator receives either full or bandit feedback according to a known probability. We give sublinear regret bounds in all cases. We numerically evaluate our algorithms on examples with thermostatically controlled loads and electric vehicles.