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Learning social preferences in games
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Reinforcement learning: a survey
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R-MAX: a general polynomial time algorithm for near-optimal reinforcement learning
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Learning equilibrium as a generalization of learning to optimize
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We propose a machine learning approach to action prediction in one-shot games. In contrast to the huge literature on learning in games where an agent's model is deduced from its previous actions in a multi-stage game, we propose the idea of inferring correlations between agents' actions in different one-shot games in order to predict an agent's action in a game which she did not play yet. We define the approach and show, using real data obtained in experiments with human subjects, the feasibility of this approach. Furthermore, we demonstrate that this method can be used to increase payoffs of an adequately informed agent. This is, to the best of our knowledge, the first proposed and tested approach for learning in one-shot games, which is the most basic form of multi-agent interaction.