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 …

Decision making is hard. It often requires reasoning about uncertain environments, partial
observability and action spaces that are too large to enumerate. In such tasks decision …

This paper describes solving multi-objective reinforcement learning problems where there
are multiple conflicting objectives with unknown weights. Reinforcement learning (RL) is a …

Many—if not most—real-world decision problems involve multiple (possibly conflicting)
objectives. If a model of the environment is not readily available, agents must interact with …

Multi-objective reinforcement learning (MORL) algorithms extend conventional
reinforcement learning (RL) to the more general case of problems with multiple, conflicting …

Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL) leads to better
learning results for network defense games. This is particularly useful for network security …

This thesis concerns various aspects of robustness in machine learning, which refers
broadly to the impact of certain modelling assumptions on a model's quality. The topic is …

ACKNOWLEDGEMENTS Firstly, I would like to express my sincere gratitude to my
supervisor Prof. Fillia Makedon for her continuous support through all these years. I have …

Abstract [eng] Reinforcement learning is one of the main paradigms of machine learning
methodologies, a technique for sequential decision making in a fully or partially observed …

The mission of this chapter is to formalize multi-objective reinforcement learning (MORL)
problems where there are multiple conflicting objectives with unknown weights. The …