Increased global competition has placed a premium on customer satisfaction, and there is a
greater demand for manufacturers to be flexible with their products and services. This …

Realizing high performance of ordinary robots is one of the core problems in robotic
research. Improving the performance of ordinary robots usually relies on the collaborative …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

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

Q-learning is arguably one of the most applied representative reinforcement learning
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …

In human-robot collaborative assembly, robots are often required to dynamically change
their pre-planned tasks to collaborate with human operators in a shared workspace …

Over the last few years there has been rapid research growth into eXplainable Artificial
Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML). Drivers for …

This article surveys reinforcement learning approaches in social robotics. Reinforcement
learning is a framework for decision-making problems in which an agent interacts through …

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective
sciences have proliferated rather than converged due to differing assumptions about what …

The past decade has seen the emergence of the use of reinforcement learning models to
study developmental change in value-based learning. It is unclear, however, whether these …