Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards. - Umer Siddique; Paul Weng; Matthieu Zimmer (ICML, 2020)

Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards. - Umer Siddique; Paul Weng; Matthieu Zimmer (CoRR, 2020)

Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning. - Matthieu Zimmer; Umer Siddique; Paul Weng (CoRR, 2020)

Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning. - Matthieu Zimmer; Claire Glanois; Umer Siddique; Paul Weng (ICML, 2021)

Fair Deep Reinforcement Learning with Generalized Gini Welfare Functions. - Guanbao Yu; Umer Siddique; Paul Weng (AAMAS Workshops, 2023)

Fair Deep Reinforcement Learning with Preferential Treatment. - Guanbao Yu; Umer Siddique; Paul Weng (ECAI, 2023)

Fairness in Preference-based Reinforcement Learning. - Umer Siddique; Abhinav Sinha; Yongcan Cao (CoRR, 2023)

Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation. - Suleiman Abahussein; Dayong Ye; Congcong Zhu; Zishuo Cheng; Umer Siddique; Sheng Shen 005 (Inf., 2023)

Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems. - Umer Siddique; Abhinav Sinha; Yongcan Cao (CoRR, 2024)

Towards Fair and Efficient Policy Learning in Cooperative Multi-Agent Reinforcement Learning. - Umer Siddique; Peilang Li; Yongcan Cao (AAMAS, 2025)

From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation. - Peilang Li; Umer Siddique; Yongcan Cao (CoRR, 2025)

From fair solutions to compromise solutions in multi-objective deep reinforcement learning. - Junqi Qian; Umer Siddique; Guanbao Yu; Paul Weng (Neural Comput. Appl., 2025)