Real-world challenges for reinforcement learning in building control
- URL: http://arxiv.org/abs/2112.06127v1
- Date: Thu, 25 Nov 2021 02:59:53 GMT
- Title: Real-world challenges for reinforcement learning in building control
- Authors: Zoltan Nagy and Kingsley Nweye
- Abstract summary: We propose a non-exhaustive nine real world challenges for reinforcement learning building controller.
We argue that building control research should be expressed in this framework.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Building upon prior research that highlighted the need for standardizing
environments for building control research, and inspired by recently introduced
benchmarks for real life reinforcement learning control, here we propose a
non-exhaustive nine real world challenges for reinforcement learning building
controller. We argue that building control research should be expressed in this
framework in addition to providing a standardized environment for
repeatability. Advanced controllers such as model predictive control and
reinforcement learning control have both advantages and disadvantages that
prevent them from being implemented in real world buildings. Comparisons
between the two are seldom, and often biased. By focusing on the benchmark
problems and challenges, we can investigate the performance of the controllers
under a variety of situations and generate a fair comparison. Lastly, we call
for a more interdisciplinary effort of the research community to address the
real world challenges, and unlock the potentials of advanced building
controllers.
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