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Opponent modeling is necessary in multi-agent settings where secondary agents with competing goals also adapt their strategies, yet it remains challenging because strategies interact with each other and change.
Adaptive mixtures of local experts
Jacobs, Robert A., Jordan, Michael I., Nowlan, Steven J., and Hinton, Geoffrey E · 1991
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Q-learning
Watkins, Christopher J. C. H. and Dayan, Peter · 1992
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Markov games as a framework for multi-agent reinforcement learning
Littman, Michael L · 1994
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Reinforcement learning: An introduction , volume 1
Sutton, Richard S and Barto, Andrew G · 1998
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Using artifical neural networks to model opponents in texas hold’em
Davidson, Aaron · 1999
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Uther, William and Veloso, Manuela · 2003
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Bayes’ bluff: Opponent modelling in poker
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Combining opponent modeling and model-based reinforcement learning in a two-player competitive game
Collins, Brian · 2007
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Lockett, Alan J., Chen, Charles L., and Miikkulainen, Risto · 2007
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Tampuu, Ardi, Matiisen, Tambet, Kodelja, Dorian, Kuzovkin, Ilya, Korjus, Kristjan, Aru, Juhan, Aru, Jaan, and Vicente, Raul · 2015
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Bard, Nolan, Johanson, Michael, Burch, Neil, and Bowling, Michael · 2013
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Opponent modeling in poker
Billings, Darse, Papp, Denis, Schaeffer, Jonathan, and Szafron, Duane
Cited in the paper.
Opponent modeling in poker
Billings, Darse, Papp, Denis, Schaeffer, Jonathan, and Szafron, Duane
Cited in the paper.
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