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Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings.
Kingsley Nweye, Bo
Liu, Nagy Zoltan, and Peter Stone.
Journal of Energy and AI, 2022,
September 2022.
Building upon prior research that highlighted the need for standardizing environments for building control research, and inspired by recently introduced challenges for real life reinforcement learning (RL) control, here we propose a non-exhaustive set of nine real world challenges for RL control in grid- interactive buildings (GIBs). We argue that research in this area should be expressed in this framework in addition to providing a standardized environment for repeatability. Advanced controllers such as model predictive control (MPC) and RL control have both advantages and disadvantages that prevent them from being implemented in real world problems. Comparisons between the two are rare, and often biased. By focusing on the challenges, we can investigate the performance of the controllers under a variety of situations and generate a fair comparison. As a demonstration, we implement the online learning challenge in CityLearn, an OpenAI Gym environment for the easy implementation of RL agents in a demand response setting to reshape the aggregated curve of electricity demand by controlling the energy storage of a diverse set of buildings in a district, and study the impact of different levels of domain knowledge and complexity of RL algorithms. We show that the sequence of operations utilized in a rule based controller (RBC) used for online training aspects the performance of the RL agents when evaluated on a set of four energy flexibility metrics. Longer online learning from an optimized RBC leads to improved performance in the long run. RL agents that learn from a simplified RBC risk poorer performance as the online learning period increases. We also observe no impact on performance from information sharing amongst agents. We call for a more interdisciplinary eport of the research community to address the real world challenges, and unlock the potential of GIB controllers.
@Article{Energy-and-AI22-Nweye,
author = {Kingsley Nweye and Bo Liu and Nagy Zoltan and Peter Stone},
title = {Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings},
journal= {Journal of Energy and AI, 2022},
location = {New York, United States},
month = {September},
year = {2022},
abstract = {
Building upon prior research that highlighted the need for standardizing environments for building
control research, and inspired by recently introduced challenges for real life reinforcement learning
(RL) control, here we propose a non-exhaustive set of nine real world challenges for RL control in grid-
interactive buildings (GIBs). We argue that research in this area should be expressed in this framework
in addition to providing a standardized environment for repeatability. Advanced controllers such as
model predictive control (MPC) and RL control have both advantages and disadvantages that prevent
them from being implemented in real world problems. Comparisons between the two are rare, and
often biased. By focusing on the challenges, we can investigate the performance of the controllers
under a variety of situations and generate a fair comparison. As a demonstration, we implement the
online learning challenge in CityLearn, an OpenAI Gym environment for the easy implementation
of RL agents in a demand response setting to reshape the aggregated curve of electricity demand
by controlling the energy storage of a diverse set of buildings in a district, and study the impact of
different levels of domain knowledge and complexity of RL algorithms. We show that the sequence of
operations utilized in a rule based controller (RBC) used for online training aspects the performance
of the RL agents when evaluated on a set of four energy flexibility metrics. Longer online learning
from an optimized RBC leads to improved performance in the long run. RL agents that learn from a
simplified RBC risk poorer performance as the online learning period increases. We also observe no
impact on performance from information sharing amongst agents. We call for a more interdisciplinary
eport of the research community to address the real world challenges, and unlock the potential of GIB
controllers.
},
}
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