Towards Robust, Efficient, and Practical Decision-Making: From Reward-Maximizing Deep Reinforcement Learning to Reward-Matching GFlowNets (original) (raw)

Authors

• Ling Pan Hong Kong University of Science and Technology

DOI:

https://doi.org/10.1609/aaai.v39i27.35118

Abstract

In this talk, I will present our recent advances in sequential decision-making systems in reward-maximizing deep RL and the emerging reward-matching GFlowNets. The presentation will examine three fundamental challenges: efficiency, robustness, and practical applications.

How to Cite

Pan, L. (2025). Towards Robust, Efficient, and Practical Decision-Making: From Reward-Maximizing Deep Reinforcement Learning to Reward-Matching GFlowNets. Proceedings of the AAAI Conference on Artificial Intelligence, 39(27), 28724-28724. https://doi.org/10.1609/aaai.v39i27.35118

Issue

Section

New Faculty Highlights