no code implementations • 26 Dec 2022 • Tatsuya Hayashi, Naoki Ito, Koji Tabata, Atsuyoshi Nakamura, Katsumasa Fujita, Yoshinori Harada, Tamiki Komatsuzaki
Classification bandits are multi-armed bandit problems whose task is to classify a given set of arms into either positive or negative class depending on whether the rate of the arms with the expected reward of at least h is not less than w for given thresholds h and w. We study a special classification bandit problem in which arms correspond to points x in d-dimensional real space with expected rewards f(x) which are generated according to a Gaussian process prior.
no code implementations • 31 Jan 2019 • Koji Tabata, Atsuyoshi Nakamura, Junya Honda, Tamiki Komatsuzaki
We study a bad arm existing checking problem in which a player's task is to judge whether a positive arm exists or not among given K arms by drawing as small number of arms as possible.
1 code implementation • 19 Nov 2018 • Aurelien Pelissier, Atsuyoshi Nakamura, Koji Tabata
Monte Carlo tree search (MCTS) has received considerable interest due to its spectacular success in the difficult problem of computer Go and also proved beneficial in a range of other domains.