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

摘要

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.