Abstract Model-based reinforcement learning aims to improve the sample efficiency of policy
learning by modeling the dynamics of the environment. Recently, the latent dynamics model …

Reinforcement learning has the potential to control stochastic nonlinear systems in optimal
manners successfully. We propose a mixed reinforcement learning (mixed RL) algorithm by …

Abstract ENGLISH SUMMARY: The efficiency and effectiveness of fuel usage in a typical
nuclear reactor is influenced by the specific arrangement of available fuel assemblies in the …

The in-core fuel management optimisation (ICFMO) problem has been studied for several
decades. Very little research has, however, been aimed at multiobjective optimisation …

In this paper, the authors present the development of an optimisation suite and its
implementation. This paper is part of an ongoing project that aims at developing a hybrid …

In stochastic simulation optimisation, several system designs are considered. These designs
are ranked in order and the best is selected based on one or more performance measures …

This paper presents the use of the cross-entropy method for multi-objective optimisation
(MOO CEM) metaheuristic and the multi-objective moonyoung yoon (MMY) procedure in a …

Society is continually in search of ways to optimise various objectives. When faced with
multiple and con icting objectives, humans are in need of solution techniques to enable …

The investigation and exploration of search and optimisation methodologies are crucial
research areas. Take for example the potential impact of an effective and computationally …

The cross-entropy method for multi-objective optimisation (MOO CEM) was recently
introduced by Bekker & Aldrich (2010) and Bekker (2012). Results presented by both show …