Beyond robustness: A taxonomy of approaches towards resilient multi-robot systems
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 …
property of robustness is often underpinned by costly requirements such as over …
A systematic survey of control techniques and applications in connected and automated vehicles
Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …
Robust multi-agent reinforcement learning with state uncertainty
In real-world multi-agent reinforcement learning (MARL) applications, agents may not have
perfect state information (eg, due to inaccurate measurement or malicious attacks), which …
perfect state information (eg, due to inaccurate measurement or malicious attacks), which …
Data-driven distributionally robust electric vehicle balancing for autonomous mobility-on-demand systems under demand and supply uncertainties
Electric vehicles (EVs) are being rapidly adopted due to their economic and societal
benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend …
benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend …
Serverless federated auprc optimization for multi-party collaborative imbalanced data mining
To address the big data challenges, serverless multi-party collaborative training has recently
attracted attention in the data mining community, since they can cut down the …
attracted attention in the data mining community, since they can cut down the …
[PDF][PDF] A robust and constrained multi-agent reinforcement learning framework for electric vehicle amod systems
Electric vehicles (EVs) play critical roles in autonomous mobility-on-demand (AMoD)
systems, but their unique charging patterns increase the model uncertainties in AMoD …
systems, but their unique charging patterns increase the model uncertainties in AMoD …
Value functions factorization with latent state information sharing in decentralized multi-agent policy gradients
H Zhou, T Lan, V Aggarwal - IEEE Transactions on Emerging …, 2023 - ieeexplore.ieee.org
The use of centralized training and decentralized execution for value function factorization
demonstrates the potential for addressing cooperative multi-agent reinforcement tasks …
demonstrates the potential for addressing cooperative multi-agent reinforcement tasks …
A multimodal graph neural network framework for cancer molecular subtype classification
Background The recent development of high-throughput sequencing has created a large
collection of multi-omics data, which enables researchers to better investigate cancer …
collection of multi-omics data, which enables researchers to better investigate cancer …
Robust electric vehicle balancing of autonomous mobility-on-demand system: A multi-agent reinforcement learning approach
Electric autonomous vehicles (EAVs) are getting attention in future autonomous mobility-on-
demand (AMoD) systems due to their economic and societal benefits. However, EAVs' …
demand (AMoD) systems due to their economic and societal benefits. However, EAVs' …
A Causal Inference Approach to Eliminate the Impacts of Interfering Factors on Traffic Performance Evaluation
Before and after study frameworks are widely adopted to evaluate the effectiveness of
transportation policies and emerging technologies. However, many factors such as seasonal …
transportation policies and emerging technologies. However, many factors such as seasonal …