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 …

To mitigate global warming and energy shortage, integration of renewable energy
generation sources, energy storage systems, and plug-in electric vehicles (PEVs) have been …

Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …

The majority of multi-agent system implementations aim to optimise agents' policies with
respect to a single objective, despite the fact that many real-world problem domains are …

The number of Electric Vehicle (EV) owners is expected to significantly increase in the near
future, since EVs are regarded as valuable assets both for transportation and energy storage …

This paper proposes a novel scalable type of multi-agent reinforcement learning-based
coordination for distributed residential energy. Cooperating agents learn to control the …

While recent work in reinforcement learning (RL) has led to agents capable of solving
increasingly complex tasks, the issue of high sample complexity is still a major concern. This …

This paper proposes a novel taxonomy of coordination strategies for distributed energy
resources at the edge of the electricity grid, based on a systematic analysis of key literature …

Autonomously learning a complex task takes a very long time for Reinforcement Learning
(RL) agents. One way to learn faster is by dividing a complex task into several simple …