3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2025
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…