11 papers
Inference-Time Policy Alignment for Fair Reinforcement Learning
Umer Siddique, Peilang Li, Conor Wallace +1
Deep reinforcement learning (RL) agents achieve strong performance by optimizing scalar reward functions. However, once deployed, the policies of these RL agents are often rigid an…
Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning
Umer Siddique, Peilang Li, Yongcan Cao
Fairness is an important aspect of decision-making in multi-objective reinforcement learning (MORL), where policies must ensure both optimality and equity across multiple, potentia…
Engagement-Zone-Aware Input-Constrained Guidance for Safe Target Interception in Contested Environments
Praveen Kumar Ranjan, Abhinav Sinha, Yongcan Cao
We address target interception in contested environments in the presence of multiple defenders whose interception capability is limited by finite ranges. Conventional methods typic…
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.…