Summary of Sinergym — a Virtual Testbed For Building Energy Optimization with Reinforcement Learning, by Alejandro Campoy-nieves et al.
SINERGYM – A virtual testbed for building energy optimization with Reinforcement Learning
by Alejandro Campoy-Nieves, Antonio Manjavacas, Javier Jiménez-Raboso, Miguel Molina-Solana, Juan Gómez-Romero
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper presents Sinergym, an open-source Python-based virtual testbed for large-scale building simulation, data collection, continuous control, and experiment monitoring. Machine learning algorithms can leverage Sinergym’s simulations to learn optimal control from vast amounts of data without supervision, particularly under the reinforcement learning paradigm. The framework provides a consistent interface for training and running controllers, predefined benchmarks, experiment visualization, replication support, and comprehensive documentation in a ready-to-use software library. Sinergym aims to address the lack of open and standardized tools hindering the widespread application of machine learning and reinforcement learning to building energy optimization (BEO). By integrating simulation, data, and control, Sinergym supports the development of intelligent, data-driven applications for more efficient and responsive building operations, aligning with the objectives of digital twin technology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a special tool called Sinergym that helps people use machine learning to make buildings work better. It does this by letting you simulate different things happening in a building, collect data from those simulations, and then use that data to control the building. This is useful because it lets you try out different ideas without actually changing anything in the real building. The tool has some special features like being able to run controllers, having predefined ways of testing things, and making it easy to share results with others. The people who made Sinergym want to help make buildings more efficient by using computers to learn what works best. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Reinforcement learning
