activity
20192023
most citedCausality-driven Hierarchical Structure Discovery for Reinforcement Learning

13 citations · 32 across the 7 of their papers we have counts for

collaborators
Showing cs.LGShow all

5 papers · 1 filter

cs.LG20223 cited

Object-Category Aware Reinforcement Learning

Qi Yi, Rui Zhang, Shaohui Peng +6

Object-oriented reinforcement learning (OORL) is a promising way to improve the sample efficiency and generalization ability over standard RL. Recent works that try to solve OORL t…

cs.LG202213 cited

Causality-driven Hierarchical Structure Discovery for Reinforcement Learning

Shaohui Peng, Xing Hu, Rui Zhang +9

Hierarchical reinforcement learning (HRL) effectively improves agents' exploration efficiency on tasks with sparse reward, with the guide of high-quality hierarchical structures (e…

cs.LG20212 cited

Hindsight Value Function for Variance Reduction in Stochastic Dynamic Environment

Jiaming Guo, Rui Zhang, Xishan Zhang +6

Policy gradient methods are appealing in deep reinforcement learning but suffer from high variance of gradient estimate. To reduce the variance, the state value function is applied…

cs.LG20202 cited

DWM: A Decomposable Winograd Method for Convolution Acceleration

Di Huang, Xishan Zhang, Rui Zhang +9

Winograd's minimal filtering algorithm has been widely used in Convolutional Neural Networks (CNNs) to reduce the number of multiplications for faster processing. However, it is on…

cs.LG20195 cited

Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Xishan Zhang, Shaoli Liu, Rui Zhang +8

Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers. Recent emerged quantization technique has been applied to inference of deep neur…