In this review, the industry's current issues regarding intelligent manufacture are presented.
This work presents the status and the potential for the I4. 0 and I5. 0's revolutionary …

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

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

With the increasing popularity of machine learning techniques, it has become common to
see prediction algorithms operating within some larger process. However, the criteria by …

We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-
objective decision problems where the relative importances of the objectives are not known …

Many real-world decision problems have multiple objectives. For example, when choosing a
medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize …

In multi-objective decision planning and learning, much attention is paid to producing
optimal solution sets that contain an optimal policy for every possible user preference profile …

Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision
problems where agents may have different preferences over (possibly conflicting) reward …

Despite growing interest over recent years in applying reinforcement learning to
multiobjective problems, there has been little research into the applicability and …

A common approach to address multiobjective problems using reinforcement learning
methods is to extend model-free, value-based algorithms such as Q-learning to use a vector …