9 citations · 20 across the 14 of their papers we have counts for
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cs.LG2022★ 2 cited
Generative Augmented Flow Networks
Ling Pan, Dinghuai Zhang, Aaron Courville +2
The Generative Flow Network is a probabilistic framework where an agent learns a stochastic policy for object generation, such that the probability of generating an object is propo…
cs.LG2022★ 9 cited
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning
Pihe Hu, Ling Pan, Yu Chen +2
Multi-user delay constrained scheduling is important in many real-world applications including wireless communication, live streaming, and cloud computing. Yet, it poses a critical…
cs.LG2022★ 2 cited
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path
Yihan Du, Siwei Wang, Longbo Huang
In this paper, we study a novel episodic risk-sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, which aims to maximize the tail of the reward-to-go at each ste…