# Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control

arXiv: https://arxiv.org/abs/2111.06025 · HTML: https://mlsanity.com/paper/2111.06025/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments](https://mlsanity.com/paper/1912.05510/) (2019)
- [A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks](https://mlsanity.com/paper/1910.02217/) (2019)
- [Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure](https://mlsanity.com/paper/1910.07899/) (2019)

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

- [Machine Learning for Smart and Energy-Efficient Buildings](https://mlsanity.com/paper/2211.14889/) (2022)

## Abstract (arXiv)

Optimizing prices for energy demand response requires a flexible controller with ability to navigate complex environments. We propose a reinforcement learning controller with surprise minimizing modifications in its architecture. We suggest that surprise minimization can be used to improve learning speed, taking advantage of predictability in peoples' energy usage. Our architecture performs well in a simulation of energy demand response. We propose this modification to improve functionality and save in a large scale experiment.