6 papers
Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference
Yiran Gao, Juntao Chen, Tao Li
Network incident response remains slow and labor-intensive as the defender must infer multi-stage attacks from partial observations and translate recovery decisions into reliable s…
In-Context Reinforcement Learning via Communicative World Models
Fernando Martinez-Lopez, Tao Li, Yingdong Lu +1
Reinforcement learning (RL) agents often struggle to generalize to new tasks and contexts without updating their parameters, mainly because their learned representations and polici…
Stackelberg Coupling of Online Representation Learning and Reinforcement Learning
Fernando Martinez, Tao Li, Yingdong Lu +1
Deep Q-learning jointly learns representations and values within monolithic networks, promising beneficial co-adaptation between features and value estimates. Although this archite…
SPRIG: Stackelberg Perception-Reinforcement Learning with Internal Game Dynamics
Fernando Martinez-Lopez, Juntao Chen, Yingdong Lu
Deep reinforcement learning agents often face challenges to effectively coordinate perception and decision-making components, particularly in environments with high-dimensional sen…
Federated Learning for Discrete Optimal Transport with Large Population under Incomplete Information
Navpreet Kaur, Juntao Chen, Yingdong Lu
Optimal transport is a powerful framework for the efficient allocation of resources between sources and targets. However, traditional models often struggle to scale effectively in…
Mean Field Control by Stochastic Koopman Operator via a Spectral Method
Yuhan Zhao, Juntao Chen, Yingdong Lu +1
Mean field control provides a robust framework for coordinating large-scale populations with complex interactions and has wide applications across diverse fields. However, the inhe…