Summary of Go-explore For Residential Energy Management, by Junlin Lu et al.
Go-Explore for Residential Energy Management
by Junlin Lu, Patrick Mannion, Karl Mason
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As a machine learning educator, I summarize this paper on applying reinforcement learning (RL) for residential energy management. The RL agents struggle with deceptive and sparse rewards, making thorough exploration crucial. Go-Explore is a family of algorithms combining planning methods and RL to achieve efficient exploration. This paper uses Go-Explore to solve the cost-saving task in residential energy management problems, achieving an improvement of up to 19.84% compared to other well-known RL algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners or general audiences, this paper is about using machine learning (ML) to help people save money on their energy bills. The ML agents have trouble figuring out what works best because the rewards they get are sometimes fake and not always available. To solve this problem, scientists developed a new way of exploring called Go-Explore. This method helps the ML agents find the best solution for saving energy costs, which is an improvement of up to 19.84% compared to previous methods. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
