2 citations · 2 across the 6 of their papers we have counts for
7 papers
ReCollab: Retrieval-Augmented LLMs for Cooperative Ad-hoc Teammate Modeling
Conor Wallace, Umer Siddique, Yongcan Cao
Ad-hoc teamwork (AHT) requires agents to infer the behavior of previously unseen teammates and adapt their policy accordingly. Conventional approaches often rely on fixed probabili…
Three-dimensional Integrated Guidance and Control for Leader-Follower Flexible Formation of Fixed Wing UAVs
Praveen Kumar Ranjan, Abhinav Sinha, Yongcan Cao
This paper presents a nonlinear integrated guidance and control (IGC) approach for flexible leader-follower formation flight of fixed-wing unmanned aerial vehicles (UAVs) while acc…
Safety-Critical Input-Constrained Nonlinear Intercept Guidance in Multiple Engagement Zones
Praveen Kumar Ranjan, Abhinav Sinha, Yongcan Cao
This paper presents an input-constrained nonlinear guidance law to address the problem of intercepting a stationary target in contested environments with multiple defending agents.…
Adaptive Event-Triggered Policy Gradient for Multi-Agent Reinforcement Learning
Umer Siddique, Abhinav Sinha, Yongcan Cao
Conventional multi-agent reinforcement learning (MARL) methods rely on time-triggered execution, where agents sample and communicate actions at fixed intervals. This approach is of…
TransAM: Transformer-Based Agent Modeling for Multi-Agent Systems via Local Trajectory Encoding
Conor Wallace, Umer Siddique, Yongcan Cao
Agent modeling is a critical component in developing effective policies within multi-agent systems, as it enables agents to form beliefs about the behaviors, intentions, and compet…
From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation
Peilang Li, Umer Siddique, Yongcan Cao
Deep reinforcement learning (RL) has shown remarkable success in complex domains, however, the inherent black box nature of deep neural network policies raises significant challeng…