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…
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…
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…
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…