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Rating strategies in a game is an important area of research in game theory and artificial intelligence, and can be applied to any real-world competitive or cooperative setting.
Mastering atari, go, chess and shogi by planning with a learned model
Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., Lillicrap, T. P., and Silver, D · 1911
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Non-cooperative games
Nash, J · 1951
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Information theory and statistical mechanics
Jaynes, E. T · 1957
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Aggregation of preference orderings
Kreweras, G · 1965
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Subjectivity and correlation in randomized strategies
Aumann, R · 1974
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The rating of chessplayers, past and present
Elo, A. E · 1978
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Strategically zero-sum games: the class of games whose completely mixed equilibria cannot be improved upon
Moulin, H. and Vial, J.-P · 1978
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Probabilistic social choice based on simple voting comparisons
Fishburn, P. C · 1984
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A General Theory of Equilibrium Selection in Games , volume 1
Harsanyi, J. and Selten, R · 1988
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Quantal response equilibria for normal form games
McKelvey, R. D. and Palfrey, T · 1995
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Calibrated learning and correlated equilibrium
Foster, D. P. and Vohra, R. V · 1997
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A simple adaptive procedure leading to correlated equilibrium
Hart, S. and Mas-Colell, A · 2000
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Analyzing complex strategic interactions in multi-agent systems
Walsh, W., Das, R., Tesauro, G., and Kephart, J · 2002
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Planning in the presence of cost functions controlled by an adversary
McMahan, H. B., Gordon, G. J., and Blum, A · 2003
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A dynamic homotopy interpretation of the logistic quantal response equilibrium correspondence
Turocy, T. L · 2005
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Prediction, Learning, and Games
Cesa-Bianchi, N. and Lugosi, G · 2006
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Reducibility among equilibrium problems
Goldberg, P. W. and Papadimitriou, C. H · 2006
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Methods for empirical game-theoretic analysis
Wellman, M. P · 2006
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Trueskill™: A bayesian skill rating system
Herbrich, R., Minka, T., and Graepel, T · 2007
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Maximum entropy correlated equilibria
Ortiz, L. E., Schapire, R. E., and Kakade, S. M · 2007
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Monte Carlo sampling for regret minimization in extensive games
Lanctot, M., Waugh, K., Zinkevich, M., and Bowling, M · 2009
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Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Shoham, Y. and Leyton-Brown, K · 2009
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Eigengame: PCA as a nash equilibrium
Gemp, I. M., McWilliams, B., Vernade, C., and Graepel, T · 2010
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Actor-critic policy optimization in partially observable multiagent environments
Srinivasan, S., Lanctot, M., Zambaldi, V., Pérolat, J., Tuyls, K., Munos, R., and Bowling, M · 2018
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Open-ended learning in symmetric zero-sum games
Balduzzi, D., Garnelo, M., Bachrach, Y., Czarnecki, W., Perolat, J., Jaderberg, M., and Graepel, T · 2019
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A survey and critique of multiagent deep reinforcement learning
Hernandez-Leal, P., Kartal, B., and Taylor, M. E · 2019
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Human-level performance in 3d multiplayer games with population-based reinforcement learning
Jaderberg, M., Czarnecki, W., Dunning, I., Marris, L., Lever, G., Castañeda, A., Beattie, C., Rabinowitz, N., Morcos, A., Ruderman, A., Sonnerat, N., Green, T., Deason, L., Leibo, J., Silver, D., Hassabis, D., Kavukcuoglu, K., and Graepel, T · 2019
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OpenSpiel: A framework for reinforcement learning in games
Lanctot, M., Lockhart, E., Lespiau, J.-B., Zambaldi, V., Upadhyay, S., Pérolat, J., Srinivasan, S., Timbers, F., Tuyls, K., Omidshafiei, S., Hennes, D., Morrill, D., Muller, P., Ewalds, T., Faulkner, R., Kramár, J., Vylder, B. D., Saeta, B., Bradbury, J., Ding, D., Borgeaud, S., Lai, M., Schrittwieser, J., Anthony, T., Hughes, E., Danihelka, I., and Ryan-Davis, J · 2019
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Evolutionary dynamics of multi-agent learning: A survey
Bloembergen, D., Tuyls, K., Hennes, D., and Kaisers, M · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Stig Petersen, C. B., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D · 2015
Cited alongside, same era.
Deep reinforcement learning from self-play in imperfect-information games
Heinrich, J. and Silver, D · 2016
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Gambit: Software tools for game theory, version 16.0.1, 2016
McKelvey, R. D., McLennan, A. M., and Turocy, T. L · 2016
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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Autocurricula and the emergence of innovation from social interaction: A manifesto for multi-agent intelligence research, 2019
Leibo, J. Z., Hughes, E., Lanctot, M., and Graepel, T · 2019
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α \alpha -rank: Multi-agent evaluation by evolution
Omidshafiei, S., Papadimitriou, C., Piliouras, G., Tuyls, K., Rowland, M., Lespiau, J.-B., Czarnecki, W. M., Lanctot, M., Perolat, J., and Munos, R · 2019
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Multiagent evaluation under incomplete information
Rowland, M., Omidshafiei, S., Tuyls, K., Perolat, J., Valko, M., Piliouras, G., and Munos, R · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Vinyals, O., Babuschkin, I., Czarnecki, W., Mathieu, M., Dudzik, A., Chung, J., Choi, D., Powell, R., Ewalds, T., Georgiev, P., Oh, J., Horgan, D., Kroiss, M., Danihelka, I., Huang, A., Sifre, L., Cai, T., Agapiou, J., Jaderberg, M., and Silver, D · 2019
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Learning to play no-press Diplomacy with best response policy iteration
Anthony, T., Eccles, T., Tacchetti, A., Kramár, J., Gemp, I., Hudson, T. C., Porcel, N., Lanctot, M., Pérolat, J., Everett, R., et al · 2020
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Real world games look like spinning tops
Czarnecki, W. M., Gidel, G., Tracey, B., Tuyls, K., Omidshafiei, S., Balduzzi, D., and Jaderberg, M · 2020
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A generalized training approach for multiagent learning
Muller, P., Omidshafiei, S., Rowland, M., Tuyls, K., Perolat, J., Liu, S., Hennes, D., Marris, L., Lanctot, M., Hughes, E., Wang, Z., Lever, G., Heess, N., Graepel, T., and Munos, R · 2020
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Bounds and dynamics for empirical game theoretic analysis
Tuyls, K., Pérolat, J., Lanctot, M., Hughes, E., Everett, R., Leibo, J. Z., Szepesvári, C., and Graepel, T · 2020
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Estimating α \alpha -rank from a few entries with low rank matrix completion
Du, Y., Yan, X., Chen, X., Wang, J., and Zhang, H · 2021
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Sample-based approximation of nash in large many-player games via gradient descent
Gemp, I. M., Savani, R., Lanctot, M., Bachrach, Y., Anthony, T. W., Everett, R., Tacchetti, A., Eccles, T., and Kramár, J · 2021
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Estimating α \alpha -rank by maximizing information gain
Rashid, T., Zhang, C., and Ciosek, K · 2021
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Zhang, K., Yang, Z., and Başar, T · 2021
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