To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …

Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …

Education is a unique area for application of artificial intelligence (AI). In this article, the
augmentation perspective and the concept of hybrid intelligence are introduced to frame our …

Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

Deep reinforcement learning (RL) agents may successfully generalize to new settings if
trained on an appropriately diverse set of environment and task configurations …

With the emergence of Industry 5.0, the human-centric manufacturing paradigm requires
manufacturing equipment (robots, etc.) interactively assist human workers to deal with …

Recent multi-task learning research argues against unitary scalarization, where training
simply minimizes the sum of the task losses. Several ad-hoc multi-task optimization …