2 citations · 2 across the 11 of their papers we have counts for
4 papers · 1 filter
Risk-Aware Input-Constrained Safe Intercept Guidance Against Multiple Moving Defenders
Praveen Kumar Ranjan, Abhinav Sinha, Yongcan Cao +2
This paper develops a risk-aware guidance law for an attacker to intercept a stationary target in the presence of multiple moving defenders while respecting the control input limit…
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…