# Go-Explore for Residential Energy Management

arXiv: https://arxiv.org/abs/2401.07710 · HTML: https://mlsanity.com/paper/2401.07710/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [First return, then explore](https://mlsanity.com/paper/2004.12919/) (2020)
- [Go-Explore: a New Approach for Hard-Exploration Problems](https://mlsanity.com/paper/1901.10995/) (2019)
- [Deep Reinforcement Learning for Smart Home Energy Management](https://mlsanity.com/paper/1909.10165/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments](https://mlsanity.com/paper/2407.11489/) (2024)
- [Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning](https://mlsanity.com/paper/2409.20258/) (2024)
- [Demonstration-Guided Continual Reinforcement Learning in Dynamic Environments](https://mlsanity.com/paper/2512.18670/) (2025)

## Abstract (arXiv)

Reinforcement learning is commonly applied in residential energy management, particularly for optimizing energy costs. However, RL agents often face challenges when dealing with deceptive and sparse rewards in the energy control domain, especially with stochastic rewards. In such situations, thorough exploration becomes crucial for learning an optimal policy. Unfortunately, the exploration mechanism can be misled by deceptive reward signals, making thorough exploration difficult. Go-Explore is a family of algorithms which combines planning methods and reinforcement learning methods to achieve efficient exploration. We use the Go-Explore algorithm to solve the cost-saving task in residential energy management problems and achieve an improvement of up to 19.84% compared to the well-known reinforcement learning algorithms.