About me
As a PhD Researcher in the Unmanned Systems Lab at the University of Texas at San Antonio, I am passionate about advancing the field of artificial intelligence, with a focus on reinforcement learning and autonomous systems. My research interests span multi-agent and multi-objective reinforcement learning, as well as the application of these techniques to control autonomous systems like drones and vehicles. I am dedicated to developing innovative algorithms that enable effective collaboration among multiple agents, balance complex objectives, and solve real-world challenges in areas such as traffic management, disaster response, and environmental monitoring. By bridging the gap between theoretical advancements and practical applications, I aim to contribute meaningful solutions that can positively impact society and further the mission of the USL in advancing unmanned systems technology.
- May 2025: Fair-PbRL accepted at the Machine Learning Journal (MLJ).
- May 2025: Three Papers accepted at the 2nd Reinforcement Learning Conference (RLC) 2025.
- March 2025: Paper accepted at the International Workshop on Multi-Agent-Based Simulation (MABS) @ AAMAS 2025.
- March 2025: Paper accepted at the Adaptive and Learning Agents (ALA) @ AAMAS 2025.
- Feb 2025: Served as a student volunteer for AAAI 2025.
- Feb 2025: Paper accepted at the American Control Conference (ACC) 2025.
- Jan 2025: Received AAAI student scholarship for AAAI 2025.
- Jan 2025: Serving as a reviewer for ICLR, ICML, NeurIPS, and IJCAI.
- December 2024: Paper accepted at AAMAS 2025 (Extended Abstract).
- December 2024: Paper accepted at the Deployable AI workshop at AAAI 2025.
- December 2024: Paper accepted at the MALTA workshop at AAAI 2025.
- November 2024: Paper accepted at Neural Computing and Applications (NCAA).
- October 2024: Released a new paper on arXiv.
- October 2024: Serving as a reviewer for ICLR and AISTATS 2024.
- August 2024: Received the RLC 2024 Registration Scholarship sponsored by the Cooperative AI Foundation.
Recent Publications
Learning fair policies in multi-objective (deep) reinforcement learning with average and discounted rewards
Siddique, Umer, Paul Weng, and Matthieu Zimmer. "Learning fair policies in multi-objective (deep) reinforcement learning with average and discounted rewards." International Conference on Machine Learning. PMLR, 2020.
Learning fair policies in decentralized cooperative multi-agent reinforcement learning
Zimmer, Matthieu, et al. "Learning fair policies in decentralized cooperative multi-agent reinforcement learning." International Conference on Machine Learning. PMLR, 2021.
Fairness in Preference-based Reinforcement Learning
Siddique, Umer, Abhinav Sinha, and Yongcan Cao. "Fairness in Preference-based Reinforcement Learning." ICML 2023 Workshop The Many Facets of Preference-Based Learning. 2023.
Fair deep reinforcement learning with preferential treatment
Yu, Guanbao, Umer Siddique, and Paul Weng. "Fair Deep Reinforcement Learning with Preferential Treatment." ECAI. 2023.
Fair Deep Reinforcement Learning with Generalized Gini Welfare Functions
Yu, Guanbao, Umer Siddique, and Paul Weng. "Fair Deep Reinforcement Learning with Generalized Gini Welfare Functions." International Conference on Autonomous Agents and Multiagent Systems. Cham: Springer Nature Switzerland, 2023.
On Deep Reinforcement Learning for Target Capture Autonomous Guidance
Siddique, Umer, Abhinav Sinha, and Yongcan Cao. "On Deep Reinforcement Learning for Target Capture Autonomous Guidance." AIAA SCITECH 2024 Forum. 2024.
Offline Reinforcement Learning with Failure Under Sparse Reward Environments
Wu, Mingkang, Umer Siddique, Abhinav Sinha, and Yongcan Cao. "Offline Reinforcement Learning with Failure Under Sparse Reward Environments." 3rd International Conference on Computing and Machine Intelligence (ICMI). 2024.
Towards Fair and Equitable Policy Learning in Cooperative Multi-Agent Reinforcement Learning
Siddique, Umer, Peilang Li, and Yongcan Cao. "Towards Fair and Equitable Policy Learning in Cooperative Multi-Agent Reinforcement Learning." Coordination and Cooperation for Multi-Agent Reinforcement Learning Methods Workshop @ RLC. 2024.