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Bandit: "Oh, good, 'cause I've been down that road before." Bandit realises something. Bandit: "Whoop, pause the game." He mimes pressing a button. Bandit: "Beep!" Cut to the bathroom. Bandit sits on the toilet, fiddling with his phone. Bluey leans on the wall, reading a book. Bingo sings as she unrolls the toilet paper.
Rug Island (Season 2, Episode 10) Bandit has to get to work; he really can't play. The girls are disappointed, but quickly recover and turn a box of felt pens into Rug Island β an island just
In everyday life, people need to make choices without full information about the environment, which poses an explore-exploit dilemma in which one must balance the need to learn about the world and the need to obtain rewards from it. The explore-exploit dilemma is often studied using the multi-armed restless bandit task, in which people repeatedly select from multiple options, and human
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This paper addresses the challenge of identifying the most appro-priate web-based content at the best time for individual users. Most βThis work was done while R. Schapire visited Yahoo! Labs. A version of this paper appears at WWW 2010, April 26-30, 2010, Raleigh, North Carolina, USA..
Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures.
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The myopic strategy is one of the most important strategies when studying bandit problems. In 2018, Nouiehed and Ross put forward a conjecture about Feldman's bandit problem (J. Appl. Prob. (2018) 55, 318-324).They proposed that for Bernoulli two-armed bandit problems, the myopic strategy stochastically maximizes the number of wins.
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