7 papers
Diffusion-Guided Uncertainty-Aware Delayed Policy Optimization
Junqi Tu, Zejiao Liu, Fangfei Li +1
Reinforcement learning in real world environments often suffers from severe performance degradation due to delayed feedback. Existing approaches typically mitigate performance degr…
Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems
Jing Liu, Yangyang Yang, Luca Ballotta +3
This paper studies multi-agent reinforcement learning with submodular team utilities for online distributed task allocation. In this setting, each agent selects one action from a l…
Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach
Jing Liu, Fangfei Li, Xin Jin +1
This paper addresses dynamic task allocation in resource-constrained multi-agent systems (MASs) with sequentially updated assignments. We develop a submodular maximization framewor…
HCPO: Hierarchical Conductor-Based Policy Optimization in Multi-Agent Reinforcement Learning
Zejiao Liu, Junqi Tu, Yitian Hong +4
In cooperative Multi-Agent Reinforcement Learning (MARL), efficient exploration is crucial for optimizing the performance of joint policy. However, existing methods often update jo…
Robust and Efficient Communication in Multi-Agent Reinforcement Learning
Zejiao Liu, Yi Li, Jiali Wang +6
Multi-agent reinforcement learning (MARL) has made significant strides in enabling coordinated behaviors among autonomous agents. However, most existing approaches assume that comm…
Secure Distributed Consensus Estimation under False Data Injection Attacks: A Defense Strategy Based on Partial Channel Coding
Jiahao Huang, Marios M. Polycarpou, Wen Yang +2
This article investigates the security issue caused by false data injection attacks in distributed estimation, wherein each sensor can construct two types of residues based on loca…