Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

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 …

Exploration in multi-agent reinforcement learning is a challenging problem, especially in
environments with sparse rewards. We propose a general method for efficient exploration by …

Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in
reinforcement learning (RL) research. However, RL systems, when applied to large-scale …

Deep Reinforcement Learning (DRL) enables agents to make decisions based on a well-
designed reward function that suites a particular environment without any prior knowledge …

Systems engineering is an ubiquitous discipline of Engineering overlapping industrial,
chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It …

Robustness is key to engineering, automation, and science as a whole. However, the
property of robustness is often underpinned by costly requirements such as over …

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

This work develops a physics-informed transfer learning framework for modeling and control
of a nonlinear process network with limited training data. Unlike the conventional transfer …