7 papers
MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination
Rui Zuo, Qinwei Huang, Mingyang Li +3
Inter-agent communication is critical for coordinating Multi-Agent Reinforcement Learning (MARL) agents under partial observability to perform effectively in cooperative games; how…
Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments
Qinwei Huang, Rui Zuo, Simon Khan +1
Conventional federated learning assumes that greater learner participation improves training performance, by leveraging abundant, independently generated local data. However, in fe…
MapATM: Enhancing HD Map Construction through Actor Trajectory Modeling
Mingyang Li, Brian Lee, Rui Zuo +3
High-definition (HD) mapping tasks, which perform lane detections and predictions, are extremely challenging due to non-ideal conditions such as view occlusions, distant lane visib…
Predictive Auxiliary Learning for Belief-based Multi-Agent Systems
Qinwei Huang, Stefan Wang, Simon Khan +2
The performance of multi-agent reinforcement learning (MARL) in partially observable environments depends on effectively aggregating information from observations, communications,…
Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning
Rui Zuo, Simon Khan, Zifan Wang +2
Reinforcement learning (RL) has demonstrated remarkable success in solving complex decision-making problems, yet its adoption in critical domains is hindered by the lack of interpr…
Linearithmic Clean-up for Vector-Symbolic Key-Value Memory with Kroneker Rotation Products
Ruipeng Liu, Qinru Qiu, Simon Khan +1
A computational bottleneck in current Vector-Symbolic Architectures (VSAs) is the ``clean-up'' step, which decodes the noisy vectors retrieved from the architecture. Clean-up typic…