# A scalable approach for real-world implementation of deep reinforcement learning controllers in buildings based on online transfer learning: The HiLo case study

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-scalable-approach-for-real-world-implementation-of-deep-reinforcement-learning/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Davide Coraci,Alberto Silvestri,Giuseppe Razzano,Davide Fop,Silvio Brandi,Esther Borkowski,Tianzhen Hong,Arno Schlueter,Alfonso Capozzoli |
| Citations | 19 |
| DOI | 10.1016/j.enbuild.2024.115254 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://doi.org/10.1016/j.enbuild.2024.115254 |
| OpenAlex ID | https://openalex.org/W4406062430 |
| Type | article |
| Year | 2025 |

## Paper authors

- [Alberto Silvestri](https://scholariq.org/researchers/alberto-silvestri/)

## Paper journal

- [Energy and Buildings](https://scholariq.org/journals/energy-and-buildings/)

## Paper primary topic

- [Building Energy and Comfort Optimization](https://scholariq.org/topics/building-energy-and-comfort-optimization/)

## Paper topics

- [Building Energy and Comfort Optimization](https://scholariq.org/topics/building-energy-and-comfort-optimization/)
- [Reinforcement Learning in Robotics](https://scholariq.org/topics/reinforcement-learning-in-robotics/)
- [Traffic control and management](https://scholariq.org/topics/traffic-control-and-management/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.