Reinforcement learning for demand response: A review of algorithms and modeling techniques

JR Vázquez-Canteli, Z Nagy - Applied energy, 2019 - Elsevier
Buildings account for about 40% of the global energy consumption. Renewable energy
resources are one possibility to mitigate the dependence of residential buildings on the …

Q-learning algorithms: A comprehensive classification and applications

B Jang, M Kim, G Harerimana, JW Kim - IEEE access, 2019 - ieeexplore.ieee.org
Q-learning is arguably one of the most applied representative reinforcement learning
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …

Incentive-based demand response for smart grid with reinforcement learning and deep neural network

R Lu, SH Hong - Applied energy, 2019 - Elsevier
Balancing electricity generation and consumption is essential for smoothing the power grids.
Any mismatch between energy supply and demand would increase costs to both the service …

A survey of learning in multiagent environments: Dealing with non-stationarity

P Hernandez-Leal, M Kaisers, T Baarslag… - arXiv preprint arXiv …, 2017 - arxiv.org
The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Coordinated energy management for a cluster of buildings through deep reinforcement learning

G Pinto, MS Piscitelli, JR Vázquez-Canteli, Z Nagy… - Energy, 2021 - Elsevier
Advanced control strategies can enable energy flexibility in buildings by enhancing on-site
renewable energy exploitation and storage operation, significantly reducing both energy …

Residential demand response: Experimental evaluation and comparison of self-organizing techniques

I Dusparic, A Taylor, A Marinescu… - … and Sustainable Energy …, 2017 - Elsevier
Residential demand response (DR) has gained a significant increase in interest from
industrial and academic communities as a means to contribute to more efficient operation of …

Traffic signal optimization through discrete and continuous reinforcement learning with robustness analysis in downtown Tehran

M Aslani, S Seipel, MS Mesgari, M Wiering - Advanced Engineering …, 2018 - Elsevier
Traffic signal control plays a pivotal role in reducing traffic congestion. Traffic signals cannot
be adequately controlled with conventional methods due to the high variations and …

Towards autonomic urban traffic control with collaborative multi-policy reinforcement learning

I Dusparic, J Monteil, V Cahill - 2016 IEEE 19th international …, 2016 - ieeexplore.ieee.org
Various multi-agent decentralized approaches based on reinforcement learning (RL) have
been proposed to increase scalability and real-time adaptiveness of urban traffic control …

An exploration strategy for non-stationary opponents

P Hernandez-Leal, Y Zhan, ME Taylor… - Autonomous Agents and …, 2017 - Springer
The success or failure of any learning algorithm is partially due to the exploration strategy it
exerts. However, most exploration strategies assume that the environment is stationary and …

Multi-agent combat in non-stationary environments

S Li, H Chi, T Xie - 2021 International Joint Conference on …, 2021 - ieeexplore.ieee.org
Multi-agent combat is a combat scenario in multiagent reinforcement learning (MARL). In
this combat, agents use reinforcement learning methods to learn optimal policies. Actually …