Summary of Reinforcement Learning For Sustainable Energy: a Survey, by Koen Ponse et al.
Reinforcement Learning for Sustainable Energy: A Survey
by Koen Ponse, Felix Kleuker, Márton Fejér, Álvaro Serra-Gómez, Aske Plaat, Thomas Moerland
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Systems and Control (eess.SY); Machine Learning (stat.ML)
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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 The paper surveys the literature on using reinforcement learning to address sustainability challenges in the energy sector. It covers the underlying research communities in both energy and machine learning, discussing relevant sustainability challenges, how they can be modeled as reinforcement learning problems, and existing solution approaches. The survey identifies overarching reinforcement learning themes that appear throughout sustainability, such as multi-agent, offline, and safe reinforcement learning. Standardization of environments is also discussed as crucial for connecting the research fields. The paper provides an extensive overview of reinforcement learning methods for sustainable energy, highlighting their potential role in the energy transition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machine learning can help make energy more sustainable. It talks about some big problems in energy, like how to manage wind farms or electric vehicle charging stations, and how reinforcement learning can be used to solve these challenges. The paper also explores different areas of machine learning that are relevant to sustainability, such as making sure multiple agents work together well or ensuring that the learning process is safe and reliable. Overall, the survey shows how machine learning can play a key role in helping us switch to sustainable energy. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
