16 citations · 19 across the 5 of their papers we have counts for
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cs.LG2022★ 1 cited
Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability
Timo Bertram, Johannes Fürnkranz, Martin Müller
In this paper, we study learning in probabilistic domains where the learner may receive incorrect labels but can improve the reliability of labels by repeatedly sampling them. In s…
cs.LG2021★ 2 cited
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning
Hongming Zhang, Ke Sun, Bo Xu +2
In deep reinforcement learning (RL) systems, abnormal states pose significant risks by potentially triggering unpredictable behaviors and unsafe actions, thus impeding the deployme…
cs.LG2019★ 16 cited
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
Chenjun Xiao, Yifan Wu, Chen Ma +2
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate. An algorit…