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

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 …

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered
robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an …

Offline reinforcement learning (RL) provides a promising direction to exploit massive amount
of offline data for complex decision-making tasks. Due to the distribution shift issue, current …

Transformer architectures have facilitated the development of large-scale and general-
purpose sequence models for prediction tasks in natural language processing and computer …

Hospital staff and resources allocation (HSRA) is a critical challenge in healthcare systems,
as it involves balancing the demands of patients, the availability of resources, and the need …

Construction robots controlled using reinforcement learning (RL) have recently emerged,
showing higher adaptability and self-learning intelligence over pre-programmed and …

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-
level control in many sequential decision-making problems, yet many open challenges still …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …