# Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids

arXiv: https://arxiv.org/abs/1910.08719 · HTML: https://mlsanity.com/paper/1910.08719/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2019.

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

- [Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS](https://mlsanity.com/paper/2102.07244/) (2021)
- [Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers](https://mlsanity.com/paper/2403.11907/) (2024)

## Abstract (arXiv)

Motivated by recent advancements in Deep Reinforcement Learning (RL), we have developed an RL agent to manage the operation of storage devices in a household and is designed to maximize demand-side cost savings. The proposed technique is data-driven, and the RL agent learns from scratch how to efficiently use the energy storage device given variable tariff structures. In most of the studies, the RL agent is considered as a black box, and how the agent has learned is often ignored. We explain the learning progression of the RL agent, and the strategies it follows based on the capacity of the storage device.