Summary of An Experimental Evaluation Of Deep Reinforcement Learning Algorithms For Hvac Control, by Antonio Manjavacas et al.
An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control
by Antonio Manjavacas, Alejandro Campoy-Nieves, Javier Jiménez-Raboso, Miguel Molina-Solana, Juan Gómez-Romero
First submitted to arxiv on: 11 Jan 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 A novel paper evaluates the performance of state-of-the-art Deep Reinforcement Learning (DRL) algorithms for Heating, Ventilation, and Air Conditioning (HVAC) control. The study compares the comfort and energy consumption outcomes of various DRL approaches, including SAC and TD3, in a standardized framework called Sinergym. The results demonstrate the potential of these algorithms to optimize HVAC systems, but also highlight challenges related to generalization and incremental learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers compared different ways to control heating, ventilation, and air conditioning (HVAC) systems using computer programs that learn from experience. They wanted to see which method works best at keeping people comfortable while saving energy. The results show that some methods are really good at doing this, but they also have some problems when things change or get more complicated. |
Keywords
* Artificial intelligence * Generalization * Reinforcement learning
