Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

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 …

A variety of Reinforcement Learning (RL) techniques blends with one or more techniques
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …

In this paper, we consider the problem of autonomous lane changing for self driving vehicles
in a multi-lane, multi-agent setting. We present a framework that demonstrates a more …

The increasing traffic congestion problem can be solved by an adaptive traffic signal control
(ATSC) system as it utilises real‐time traffic information to control traffic signals. Recently …

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 …

Dynamic radar networks (DRNs), usually composed of flying unmanned aerial vehicles
(UAVs), have recently attracted great interest for time-critical applications, such as search …

In this work we present a method for using Deep Q-Networks (DQNs) in multi-objective
environments. Deep Q-Networks provide remarkable performance in single objective …

Ensemble techniques are a powerful approach to creating better decision makers in
machine learning. Multiple decision makers are trained to solve a given task, grouped in an …