Publications (8)
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training
Pihe Hu, Shaolong Li, Zhuoran Li +2
Deep Multi-agent Reinforcement Learning (MARL) relies on neural networks with numerous parameters in multi-agent scenarios, often incurring substantial computational overhead. Cons…
Optimal Hybrid Full-Duplex/Half-Duplex Scheme for Buffer Aided Relay Systems
Cheng Li, Bin Xia, Pihe Hu +1
Full-duplex (FD) communication has received great interest in recent years due to the potential of doubling the spectral efficiency. However, how to alleviate the detrimental effec…
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
Pihe Hu, Yu Chen, Longbo Huang
We study reinforcement learning with linear function approximation where the transition probability and reward functions are linear with respect to a feature mapping $\boldsymbolÏ…
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…
Mixed Sparsity Training: Achieving 4 FLOP Reduction for Transformer Pretraining
Pihe Hu, Shaolong Li, Longbo Huang
Large language models (LLMs) have made significant strides in complex tasks, yet their widespread adoption is impeded by substantial computational demands. With hundreds of billion…
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…
Optimal Multi-User Scheduling of Buffer-Aided Relay Systems
Pihe Hu, Cheng Li, Dingjie Xu +1
Multi-User scheduling is a challenging problem under the relaying scenarios. Traditional schemes, which are based on the instantaneous signal-to-interference-plus-noises ratios (SI…
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
Yu Chen, Yihan Du, Pihe Hu +3
Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk. In this paper, we present a novel risk-sensitive RL framework that e…