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Found 4 papers, 1 papers with code
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess
In this work, we adapt a training approach inspired by the original AlphaGo system to play the imperfect information game of Reconnaissance Blind Chess.
Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability
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.
A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems
We discuss and compare two different Siamese network architectures for this task: a twin network that compares the two sets resulting after the addition, and a triplet network that models the contribution of each candidate to the existing set.
Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking
Drafting, i. e., the selection of a subset of items from a larger candidate set, is a key element of many games and related problems.
