Human-in-the-loop for reinforcement learning (RL) is usually employed to overcome the
challenge of sample inefficiency, in which the human expert provides advice for the agent …

Most existing studies on joint optimization of manufacturing systems (MS) focus on small-
scale systems with simple structures, such as the single-machine, simple serial, or parallel …

We focus on learning the zero-constraint-violation safe policy in model-free reinforcement
learning (RL). Existing model-free RL studies mostly use the posterior penalty to penalize …

Safe reinforcement learning (RL) has shown great potential for building safe general-
purpose robotic systems. While many existing works have focused on post-training policy …

Cyber-Physical Systems (CPSs) are increasingly prevalent across various industrial and
daily-life domains, with applications ranging from robotic operations to autonomous driving …

Distributed energy resources and uneven load allocation cause the three-phase unbalance
in distribution networks, which may harm the health of power equipment and increase the …

Motion planning algorithms for automated vehicles need to assess the intended behavior of
other Traffic Participants (TPs), in order to predict the likely future trajectory of TPs and plan …

Human guidance is often desired in reinforcement learning to improve the performance of
the learning agent. However, human insights are often mere opinions and educated …

Although deep reinforcement learning has demonstrated impressive achievements in
controlling various autonomous systems, eg, autonomous vehicles or humanoid robots, its …

Train trajectory optimization (TTO) is an effective way to address energy consumption in rail
transit. Reinforcement learning (RL), an excellent optimization method, has been used to …