Practical implementation and evaluation of deep reinforcement learning control for a radiant heating system-Reference-Cited by-同舟云学术

Practical implementation and evaluation of deep reinforcement learning control for a radiant heating system

Published:2018-11-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 5th Conference on Systems for Built Environments
language:
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Author:

Zhang Zhiang¹,Lam Khee Poh¹

Affiliation:

1. Carnegie Mellon University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3276774.3276775

Reference40 articles.

1. American Society of Heating Refrigerating and Air-Conditioning Engineers 2002. Guideline 14 Measurement of Energy and Demand Savings. Guideline. American Society of Heating Refrigerating and Air-Conditioning Engineers 2002. Guideline 14 Measurement of Energy and Demand Savings. Guideline.

2. American Society of Heating Refrigerating and Air-Conditioning Engineers 2017. Standard 55 Thermal Environmental Conditions for Human Occupancy. Standard. American Society of Heating Refrigerating and Air-Conditioning Engineers 2017. Standard 55 Thermal Environmental Conditions for Human Occupancy. Standard.

3. Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. CoRR abs/1606.01540 (2016). arXiv:1606.01540 Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. CoRR abs/1606.01540 (2016). arXiv:1606.01540

4. A Limited Memory Algorithm for Bound Constrained Optimization

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