Learning to play

How researchers see this paper

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Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization (2021, background): “Besides, RPG helps train adaptive policies against a set of opponents, which is related to Bayesian games (Dekel et al., 2004; Hartline et al., 2015).”
Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games (2019, background): “For example, in Bayesian game theory, a Bayesian-Nash Equilibrium is well-defined given a joint equilibrium policy profile assuming perfect rationality [6].”
When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning (2019, methodology): “’s successful communication sequence of dialog remembered by both agents to solve the Guess Who game (Jorge et al. 2016).”
Autonomous agents modelling other agents: A comprehensive survey and open problems (2017, background): “) Subsequent works studied the impact of prior beliefs on equilibrium convergence and showed that if players have different prior beliefs, their play may converge to a subjective equilibrium which is not a Nash equilibrium (Dekel et al., 2004; Nyarko, 1998).”