3 citations · 5 across the 8 of their papers we have counts for
5 papers · 1 filter
State-Novelty Guided Action Persistence in Deep Reinforcement Learning
Jianshu Hu, Paul Weng, Yutong Ban
While a powerful and promising approach, deep reinforcement learning (DRL) still suffers from sample inefficiency, which can be notably improved by resorting to more sophisticated…
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach
Yunpeng Jiang, Yutong Ban, Paul Weng
Data augmentation is widely applied and has shown its benefits in different machine learning tasks. However, as recently observed, it may have an unfair effect in multi-class class…
Revisiting Data Augmentation in Deep Reinforcement Learning
Jianshu Hu, Yunpeng Jiang, Paul Weng
Various data augmentation techniques have been recently proposed in image-based deep reinforcement learning (DRL). Although they empirically demonstrate the effectiveness of data a…
INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer
Han Fang, Zhihao Song, Paul Weng +1
Recently, deep reinforcement learning has shown promising results for learning fast heuristics to solve routing problems. Meanwhile, most of the solvers suffer from generalizing to…
Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning
Zhaohui Jiang, Paul Weng
To improve the sample efficiency of vision-based deep reinforcement learning (RL), we propose a novel method, called SPIRL, to automatically extract important patches from input im…