7 citations · 29 across the 16 of their papers we have counts for
17 papers
Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings
Hongpeng Cao, Yanbing Mao, Lui Sha +1
This paper proposes the Phy-DRL: a physics-regulated deep reinforcement learning (DRL) framework for safety-critical autonomous systems. The Phy-DRL has three distinguished invaria…
Physical Deep Reinforcement Learning Towards Safety Guarantee
Hongpeng Cao, Yanbing Mao, Lui Sha +1
Deep reinforcement learning (DRL) has achieved tremendous success in many complex decision-making tasks of autonomous systems with high-dimensional state and/or action spaces. Howe…
Phy-Taylor: Physics-Model-Based Deep Neural Networks
Yanbing Mao, Lui Sha, Huajie Shao +3
Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To ad…
Cost Function Learning in Memorized Social Networks with Cognitive Behavioral Asymmetry
Yanbing Mao, Jining Li, Naira Hovakimyan +2
This paper investigates the cost function learning in social information networks, wherein the influence of humans' memory on information consumption is explicitly taken into accou…
Dynamics of Public Opinion Evolution with Asymmetric Cognitive Bias
Yanbing Mao, Naira Hovakimyan, Tarek Abdelzaher
In this paper, we propose a pubic opinion model with incorporation of asymmetric cognitive bias: confirmation bias and negativity bias. We then investigate the generic modeling gui…
Social System Inference from Noisy Observations
Yanbing Mao, Naira Hovakimyan, Tarek Abdelzaher +1
This paper studies social system inference from a single trajectory of public evolving opinions, wherein observation noise leads to the statistical dependence of samples on time an…