Opponent Modelling by Sequence Prediction and Lookahead in Two-Player Games

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How researchers see this paper

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Learning with Opponent-Learning Awareness (2017, background): “Mealing and Shapiro (2013) propose a method that finds a policy based on predicting the opponent’s future action.”
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments (2017, background): “Modeling and exploiting opponents [29, 30] or event their learning processes [31] is advantageous as it improves convergence or helps to discover equilibria of certain properties (e.g., leads to cooperative behavior).”
Optimizing for the Future in Non-Stationary MDPs (2020, background): “…in games has been shown to be useful and regret bounds for multi-player games where players can be replaced with some probability p , i.e., the game changes slowly over time, has also been established (Zhang and Lesser, 2010; Mealing and Shapiro, 2013; Foster et al., 2016; Foerster et al., 2018).”