16 citations · 19 across the 5 of their papers we have counts for
5 papers
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess
Timo Bertram, Johannes Fürnkranz, Martin Müller
In this work, we adapt a training approach inspired by the original AlphaGo system to play the imperfect information game of Reconnaissance Blind Chess. Using only the observations…
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…
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…
A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems
Timo Bertram, Johannes Fürnkranz, Martin Müller
In this paper, we study the problem of evaluating the addition of elements to a set. This problem is difficult, because it can, in the general case, not be reduced to unconditional…
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…