Foster, D. P. and Vohra, R. Regret in the on-line decision problem. Games and Economic Behavior, 29(1):7-35, 1999.
Gibson, R. G. Regret minimization in games and the development of champion multiplayer computer poker-playing agents. Ph.D. Thesis, 2014.
Greenwald, A., Jafari, A., and Marks, C. No-  $\phi$  -regret: A connection between computational learning theory and game theory. Games, Norms and Reasons: Logic at the Crossroads, 2011.
Hart, S. and Mas-Colell, A. A simple adaptive procedure leading to correlated equilibrium. *Econometrica*, 68(5):1127-1150, 2000.
Hu, H., Lerer, A., Peysakhovich, A., and Foerster, J. "other-play" for zero-shot coordination. International Conference on Machine Learning, 2020.
Jiang, A. X. and Leyton-Brown, K. A general framework for computing optimal correlated equilibria in compact games. _Internet and Network Economics: 7th International Workshop_, WINE 2011, 2011.
Jin, C., Liu, Q., Wang, Y., and Yu, T. V-learning-a simple, efficient, decentralized algorithm for multiagent rl. arXiv preprint arXiv:2110.14555, 2021.
Kearns, M., Littman, M. L., and Singh, S. Graphical models for game theory, 2013.
Kuhn, H. W. Extensive games and the problem of information. Contributions to the Theory of Games, 2(28):193-216, 1953.
Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Perolat, J., Silver, D., and Graepel, T. A unified game-theoretic approach to multiagent reinforcement learning. Neural Information Processing Systems, 2017.
Lanctot, M., Lockhart, E., Lespiau, J.-B., Zambaldi, V., Upadhyay, S., Pérolat, J., Srinivasan, S., Timbers, F., Tuyls, K., Omidshafiei, S., et al. Openspiel: A framework for reinforcement learning in games. arXiv preprint arXiv:1908.09453, 2019.
Liu, Q., Yu, T., Bai, Y., and Jin, C. A sharp analysis of model-based reinforcement learning with self-play. International Conference on Machine Learning, 2021.
Marris, L., Muller, P., Lanctot, M., Tuyls, K., and Graepel, T. Multi-agent training beyond zero-sum with correlated equilibrium meta-solvers. International Conference on Machine Learning, 2021.
Matignon, L., Laurent, G. J., and Le Fort-Piat, N. Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems. The Knowledge Engineering Review, 27(1):1-31, 2012.
Moravcik, M., Schmid, M., Burch, N., Lisy, V., Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M., and Bowling, M. Deepstack: Expert-level artificial intelligence in heads-up no-limit poker. Science, 356(6337):508-513, 2017.
Morrill, D., D'Orazio, R., Lanctot, M., Wright, J. R., Bowling, M., and Greenwald, A. R. Efficient deviation types and learning for hindsight rationality in extensive-form games. International Conference on Machine Learning, 2021a.
Morrill, D., D'Orazio, R., Sarfati, R., Lanctot, M., Wright, J. R., Greenwald, A. R., and Bowling, M. Hindsight and sequential rationality of correlated play. AAAI Conference on Artificial Intelligence, 2021b.
Moulin, H. and Vial, J. Strategically zero-sum games: The class of games whose completely mixed equilibria cannot be improved upon. International Journal of Game Theory, 7(3):201-221, 1978.
Paquette, P., Lu, Y., Bocco, S. S., Smith, M., O-G, S., Kummerfeld, J. K., Pineau, J., Singh, S., and Courville, A. C. No-press diplomacy: Modeling multi-agent gameplay. *Neural Information Processing Systems*, 2019.