2 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…
Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems
Umer Siddique, Abhinav Sinha, Yongcan Cao
In this paper, we propose an adaptive event-triggered reinforcement learning control for continuous-time nonlinear systems, subject to bounded uncertainties, characterized by compl…