The area of building energy management has received a significant amount of interest in
recent years. This area is concerned with combining advancements in sensor technologies …

In this paper, an Improved version of the Slime Mould Algorithm (ISMA) is proposed and
applied to efficiently solve the single-and bi-objective Economic and Emission Dispatch …

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 …

Decision-making complexity, in a distributed environment, is due to hard tasks that a system
must resolve. This complexity makes researchers focus on looking for solutions to cope with …

We study a bilevel economic system, which we refer to as a Markov exchange economy
(MEE), from the point of view of multi-agent reinforcement learning (MARL). An MEE …

Multi-objective optimisation has received considerable attention in recent years as many
real world problems have multiple conflicting objectives. There is an additional layer of …

A unified distributed reinforcement learning (RL) solution is offered for both static and
dynamic economic dispatch problems (EDPs). Each agent is assigned with a fixed, discrete …

The majority of multi-agent reinforcement learning (MARL) implementations aim to optimize
systems with respect to a single objective, despite the fact that many real-world problems are …

Reinforcement Learning (RL) is a powerful and well-studied Machine Learning paradigm,
where an agent learns to improve its performance in an environment by maximising a …

This paper describes the application of reinforcement learning (RL) to multi-product
inventory management in supply chains. The problem description and solution are both …