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The utilization of artificial intelligence (AI) in card games has been a well-explored subject within AI research for an extensive period.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International Conference on Machine Learning (ICML) , 2016, pp. 1928–1937
1937
Earlier work this paper cites.
R. S. Sutton, D. A. McAllester, S. P. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” in Advances in Neural Information Processing Systems (NeurIPS) , 2000
2000
Earlier work this paper cites.
T. W. Neller and M. Lanctot, “An introduction to counterfactual regret minimization,” in Educational Advances in Artificial Intelligence (EAAI) , vol. 11, 2013
2013
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015
2015
Earlier work this paper cites.
M. Bowling, N. Burch, M. Johanson, and O. Tammelin, “Heads-up limit hold’em poker is solved,” Science , vol. 347, no. 6218, pp. 145–149, 2015
2015
Earlier work this paper cites.
J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz, “Trust region policy optimization,” in International Conference on Machine Learning (ICML) , 2015, pp. 1889–1897
2015
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al. , “Mastering the game of go with deep neural networks and tree search,” Nature , vol. 529, no. 7587, pp. 484–489, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
H. He, J. Boyd-Graber, K. Kwok, and H. Daumé III, “Opponent modeling in deep reinforcement learning,” in International Conference on Machine Learning (ICML) , 2016
2016
Earlier work this paper cites.
J. Schulman, P. Moritz, S. Levine, M. Jordan, and P. Abbeel, “High-dimensional continuous control using generalized advantage estimation,” in International Conference on Learning Representations (ICLR) , 2016
2016
Earlier work this paper cites.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton et al. , “Mastering the game of go without human knowledge,” Nature , vol. 550, no. 7676, pp. 354–359, 2017
2017
Earlier work this paper cites.
M. Moravčík, M. Schmid, N. Burch, V. Lisỳ, D. Morrill, N. Bard, T. Davis, K. Waugh, M. Johanson, and M. Bowling, “Deepstack: Expert-level artificial intelligence in heads-up no-limit poker,” Science , vol. 356, no. 6337, pp. 508–513, 2017
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
I. P. Pinto and L. R. Coutinho, “Hierarchical reinforcement learning with monte carlo tree search in computer fighting game,” IEEE Transactions on Games (TOG) , vol. 11, no. 3, pp. 290–295, 2018
2018
Cited alongside, same era.
C. Silva, R. O. Moraes, L. H. Lelis, and K. Gal, “Strategy generation for multi-unit real-time games via voting,” IEEE Transactions on Games (TOG) , vol. 11, no. 4, pp. 426–435, 2018
2018
Cited alongside, same era.
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel et al. , “A general reinforcement learning algorithm that masters chess, shogi, and go through self-play,” Science , vol. 362, no. 6419, pp. 1140–1144, 2018
2018
Cited alongside, same era.
N. Brown, A. Lerer, S. Gross, and T. Sandholm, “Deep counterfactual regret minimization,” in International Conference on Machine Learning (ICML) . PMLR, 2019, pp. 793–802
2019
Later among the works it cites.
2019
Later among the works it cites.
2020
Later among the works it cites.
Y. You, L. Li, B. Guo, W. Wang, and C. Lu, “Combinatorial q-learning for dou di zhu,” in AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) , vol. 16, no. 1, 2020, pp. 301–307
2020
Later among the works it cites.
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N. Brown and T. Sandholm, “Superhuman ai for heads-up no-limit poker: Libratus beats top professionals,” Science , vol. 359, no. 6374, pp. 418–424, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction , 2018
2018
Cited alongside, same era.
S. J. Knegt, M. M. Drugan, and M. A. Wiering, “Opponent modelling in the game of tron using reinforcement learning.” in International Conference on Agents and Artificial Intelligence (ICAART) , 2018, pp. 29–40
2018
Cited alongside, same era.
D. Perez-Liebana, J. Liu, A. Khalifa, R. D. Gaina, J. Togelius, and S. M. Lucas, “General video game ai: A multitrack framework for evaluating agents, games, and content generation algorithms,” IEEE Transactions on Games (TOG) , vol. 11, no. 3, pp. 195–214, 2019
2019
Cited alongside, same era.
O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev et al. , “Grandmaster level in starcraft II using multi-agent reinforcement learning,” Nature , vol. 575, no. 7782, pp. 350–354, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Q. Jiang, K. Li, B. Du, H. Chen, and H. Fang, “Deltadou: Expert-level doudizhu ai through self-play.” in International Joint Conferences on Artificial Intelligence (IJCAI) , 2019, pp. 1265–1271
2019
Cited alongside, same era.
H. Shen, L. Wu, Y. Li, and X. Li, “Imperfect and cooperative guandan game system,” in 2020 Chinese Control And Decision Conference (CCDC) . IEEE, 2020, pp. 226–230
2020
Later among the works it cites.
H. Li, K. Hu, S. Zhang, Y. Qi, and L. Song, “Double neural counterfactual regret minimization,” in International Conference on Learning Representations (ICLR) , 2020
2020
Later among the works it cites.
D. Ye, Z. Liu, M. Sun, B. Shi, P. Zhao, H. Wu, H. Yu, S. Yang, X. Wu, Q. Guo et al. , “Mastering complex control in MOBA games with deep reinforcement learning.” in AAAI Conference on Artificial Intelligence (AAAI) , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
D. Zha, J. Xie, W. Ma, S. Zhang, X. Lian, X. Hu, and J. Liu, “Douzero: Mastering doudizhu with self-play deep reinforcement learning,” in International Conference on Machine Learning (ICML) . PMLR, 2021, pp. 12 333–12 344
2021
Later among the works it cites.
Y. Zhao, J. Zhao, X. Hu, W. Zhou, and H. Li, “Douzero+: Improving doudizhu ai by opponent modeling and coach-guided learning,” in IEEE Conference on Games (COG) , 2022, pp. 127–134
2022
Later among the works it cites.