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1. Player-compatible learning and player-compatible equilibrium

   https://doi.org/10.1016/j.jet.2021.105238  

   Fudenberg, Drew; He, Kevin (June 2021, Journal of Economic Theory)

   null (Ed.)
2. Stochastic Multi-Player Bandit Learning from Player-Dependent Feedback

   Wang, Z.; Singh, M.K.; Zhang, C.; Riek, L.D.; Chaudhuri, K. (January 2020, ICML Workshop on Real World Experiment Design and Active Learning)

   We investigate robust data aggregation in a multi-agent online learning setting. In reality, multiple online learning agents are often deployed to perform similar tasks and receive similar feedback. We study how agents can improve their collective performance by sharing information among each other. In this paper, we formulate the ε-multi-player multi-armed bandit problem, in which a set of M players that have similar reward distributions for each arm play concurrently. We develop an upper confidence bound-based algorithm that adaptively aggregates rewards collected by different players. To our best knowledge, we are the first to develop such a scheme in a multi-player bandit learning setting. We show that under the assumption that pairwise distances between the means of the player-dependent distributions for each arm are small, we improve the (collective) regret bound by nearly a factor of M , in comparison with a baseline algorithm in which the players learn individually using the UCB-1 algorithm (Auer et al., 2002). Our algorithm also exhibits a fallback guarantee, namely, if our task similarity assumption fails to hold, our algorithm still has a performance guarantee that cannot be worse than the baseline by a constant factor. Empirically, we validate our algorithm on synthetic data. 

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3. Extra-gradient with player sampling for provable fast convergence in n-player games

   Jelassi, Samy; Domingo-Enrich, Carles; Scieur, Damien; Mensch, Arthur; Bruna, Joan (January 2020, international conference on machine learning)

   Data-driven modeling increasingly requires to find a Nash equilibrium in multi-player games, e.g. when training GANs. In this paper, we analyse a new extra-gradient method for Nash equilibrium finding, that performs gradient extrapolations and updates on a random subset of players at each iteration. This approach provably exhibits a better rate of convergence than full extra-gradient for non-smooth convex games with noisy gradient oracle. We propose an additional variance reduction mechanism to obtain speed-ups in smooth convex games. Our approach makes extrapolation amenable to massive multiplayer settings, and brings empirical speed-ups, in particular when using a heuristic cyclic sampling scheme. Most importantly, it allows to train faster and better GANs and mixtures of GANs. 

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4. Experience Management in Multi-player Games

   Zhu, Jichen; Ontañón, Santiago (January 2019, Proceedings of the 2019 IEEE Conference on Games)

   Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals. Although it has been explored for several decades, existing work in experience management has mostly focused on single-player experiences. This paper is a first attempt at identifying the main challenges to expand EM to multi-player/multi-user games or experiences. We also make connections to related areas where solutions for similar problems have been proposed (especially group recommender systems) and discusses the potential impact and applications of multi-player EM. 

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5. Player Modeling via Multi-armed Bandits

   https://doi.org/10.1145/3402942.3402952  

   Gray, Robert; Zhu, Jichen; Arigo, Dani; Forman, Evan; Ontañón, Santiago (January 2020, Proceedings of the Fifteenth International Conference on the Foundations of Digital Games (FDG ’20))