16 citations · 62 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
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…
RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
Yiqin Tan, Pihe Hu, Ling Pan +2
Training deep reinforcement learning (DRL) models usually requires high computation costs. Therefore, compressing DRL models possesses immense potential for training acceleration a…
Network Topology Optimization via Deep Reinforcement Learning
Zhuoran Li, Xing Wang, Ling Pan +5
Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the c…