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 …
resources are one possibility to mitigate the dependence of residential buildings on the …
Q-learning algorithms: A comprehensive classification and applications
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 …
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 …
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
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 …
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
Advanced control strategies can enable energy flexibility in buildings by enhancing on-site
renewable energy exploitation and storage operation, significantly reducing both energy …
renewable energy exploitation and storage operation, significantly reducing both energy …
Residential demand response: Experimental evaluation and comparison of self-organizing techniques
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 …
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
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 …
be adequately controlled with conventional methods due to the high variations and …
Towards autonomic urban traffic control with collaborative multi-policy reinforcement learning
Various multi-agent decentralized approaches based on reinforcement learning (RL) have
been proposed to increase scalability and real-time adaptiveness of urban traffic control …
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 …
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 …
this combat, agents use reinforcement learning methods to learn optimal policies. Actually …