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ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret. McAleer, S. M., Farina, G., Lanctot, M. and Sandholm, T. (2023) The Eleventh International Conference on Learning Representations.

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Abstracting Imperfect Information Away from Two-Player Zero-Sum Games. Sokota, S., D'Orazio, R., Ling, C. K., Wu, D. J., Kolter, J. Z. and Brown, N. (2023) Proceedings of the 40th International Conference on Machine Learning.

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A Generalized Minimax Q-learning Algorithm for Two-Player Zero-Sum Stochastic Games. Diddigi, R. B., Kamanchi, C. and Bhatnagar, S. (2022)

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Improving AlphaZero Using Monte-Carlo Graph Search. Czech, J., Korus, P. and Kersting, K. (2021) Proceedings of the International Conference on Automated Planning and Scheduling, 31(1):103–111.

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From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. Perolat, J., Munos, R., Lespiau, J., Omidshafiei, S., Rowland, M., Ortega, P., Burch, N., Anthony, T., Balduzzi, D., De Vylder, B., Piliouras, G., Lanctot, M. and Tuyls, K. (2021) Proceedings of the 38th International Conference on Machine Learning, pages 8525–8535.

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Alpha-Beta Pruning for Games with Simultaneous Moves. Saffidine, A., Finnsson, H. and Buro, M. (2021) Proceedings of the AAAI Conference on Artificial Intelligence, 26(1):556–562.

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An Overview of Multi-agent Reinforcement Learning from Game Theoretical Perspective. Yang, Y. and Wang, J. (2021)

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Combining Deep Reinforcement Learning and Search for Imperfect-Information Games. Brown, N., Bakhtin, A., Lerer, A. and Gong, Q. (2020) Advances in Neural Information Processing Systems, pages 17057–17069.

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Human-Level Performance in No-Press Diplomacy via Equilibrium Search. Gray, J., Lerer, A., Bakhtin, A. and Brown, N. (2020) arXiv:2010.02923 [cs.AI].

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Analysis of Hannan consistent selection for Monte Carlo tree search in simultaneous move games. Kovařík, V. and Lisý, V. (2020) Machine Learning, 109(1):1–50.

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Improving Policies via Search in Cooperative Partially Observable Games. Lerer, A., Hu, H., Foerster, J. and Brown, N. (2020) Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):7187-7194.

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Matrix games with bandit feedback. O'Donoghue, B., Lattimore, T. and Osband, I. (2020) Conference on Uncertainty in Artificial Intelligence.

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DREAM: Deep Regret minimization with Advantage baselines and Model-free learning. Steinberger, E., Lerer, A. and Brown, N. (2020)

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Towards Playing Full MOBA Games with Deep Reinforcement Learning. Ye, D., Chen, G., Zhang, W., Chen, S., Yuan, B., Liu, B., Chen, J., Liu, Z., Qiu, F., Yu, H., Yin, Y., Shi, B., Wang, L., Shi, T., Fu, Q., Yang, W., Huang, L. and Liu, W. (2020)

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The Hanabi challenge: A new frontier for AI research. Bard, N., Foerster, J. N., Chandar, S., Burch, N., Lanctot, M., Song, H. F., Parisotto, E., Dumoulin, V., Moitra, S., Hughes, E., Dunning, I., Mourad, S., Larochelle, H., Bellemare, M. G. and Bowling, M. (2020) Artificial Intelligence, 280.

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Superhuman AI for multiplayer poker. Brown, N. and Sandholm, T. (2019) Science, 365(6456):885-890. (Includes supplementary text).

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Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. Foerster, J., Song, F., Hughes, E., Burch, N., Dunning, I., Whiteson, S., Botvinick, M. and Bowling, M. (2019) arXiv:1811.01458 [cs.MA].

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Grandmaster level in StarCraft II using multi-agent reinforcement learning. Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., Oh, J., Horgan, D., Kroiss, M., Danihelka, I., Huang, A., Sifre, L., Cai, T., Agapiou, J. P., Jaderberg, M., Vezhnevets, A. S., Leblond, R., Pohlen, T., Dalibard, V., Budden, D., Sulsky, Y., Molloy, J., Paine, T. L., Gulcehre, C., Wang, Z., Pfaff, T., Wu, Y., Ring, R., Yogatama, D., Wünsch, D., McKinney, K., Smith, O., Schaul, T., Lillicrap, T., Kavukcuoglu, K., Hassabis, D., Apps, C. and Silver, D. (2019) Nature, 575(7782):350-354.

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Accelerating Self-Play Learning in Go. Wu, D. J. (2019) arXiv:1902.10565 [cs.LG].

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IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., Legg, S. and Kavukcuoglu, K. (2018)

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A Tutorial on Thompson Sampling. Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I. and Wen, Z. (2018) Found. Trends Mach. Learn., 11(1):1–96.

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Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines. Schmid, M., Burch, N., Lanctot, M., Moravčík, M., Kadlec, R. and Bowling, M. (2018)

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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K. and Hassabis, D. (2018) Science, 362(6419):1140–1144. (Includes supplementary text).

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Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms. Świechowski, M., Tajmajer, T. and Janusz, A. (2018) arXiv:1808.04794 [cs.AI].

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Superhuman AI for heads-up no-limit poker: Libratus beats top professionals. Brown, N. and Sandholm, T. (2017) Science, 359(6374):418-424. (Includes supplementary text).

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DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Moravčík, M., Schmid, M., Burch, N., Lisý, V., Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M. and Bowling, M. (2017) Science, 356(6337):508-513.

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An Introduction to Counterfactual Regret Minimization. Neller, T. W. and Lanctot, M. (2017)

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Mastering the game of Go without human knowledge. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., Driessche, G., Graepel, T. and Hassabis, D. (2017) Nature, 550(7676):354–359.

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Hash Embeddings for Efficient Word Representations. Svenstrup, D., Hansen, J. M. and Winther, O. (2017)

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Confidence Bound Algorithms in Game Trees. Volkert, M. (2017) Master's thesis, Universiteit Leiden, Leiden, Netherlands.

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Time Management for Monte Carlo Tree Search. Baier, H. and Winands, M. H. M. (2016) IEEE Transactions on Computational Intelligence and AI in Games, 8(3):301–314.

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Algorithms for Computing Strategies in Two-Player Simultaneous Move Games. Bošanský, B., Lisý, V., Lanctot, M., Cermak, J. and Winands, M. H. M. (2016) Artificial Intelligence, 237:1-40.

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Deep Reinforcement Learning from Self-Play in Imperfect-Information Games. Heinrich, J. and Silver, D. (2016)

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A New Method for Parallel Monte Carlo Tree Search. Mirsoleimani, S. A., Plaat, A., Herik, H. and Vermaseren, J. (2016) arXiv:1605.04447 [cs.DC].

[Silver 2016]

Mastering the game of Go with deep neural networks and tree search. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T. and Hassabis, D. (2016) Nature, 529(7587):484–489.

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Heads-up limit hold’em poker is solved. Bowling, M., Burch, N., Johanson, M. and Tammelin, O. (2015) Science, 347(6218):145–149.

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Deep Recurrent Q-Learning for Partially Observable MDPs. Hausknecht, M. J. and Stone, P. (2015) arXiv:1507.06527 [cs.LG].

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Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games. Lisý, V., Lanctot, M. and Bowling, M. (2015) Adaptive Agents and Multi-Agent Systems.

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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., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S. and Hassabis, D. (2015) Nature, 518(7540):529–533.

[Schaul 2015]

Deep Recurrent Q-Learning for Partially Observable MDPs. Schaul, T., Quan, J., Antonoglou, I. and Silver, D. (2015) arXiv:1511.05952 [cs.LG].

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Better Computer Go Player with Neural Network and Long-term Prediction. Tian, Y. and Zhu, Y. (2015) arXiv:1511.06410 [cs.LG].

[Maddison 2014]

Move Evaluation in Go Using Deep Convolutional Neural Networks. Maddison, C. J., Huang, A., Sutskever, I. and Silver, D. (2014) arXiv:1412.6564 [cs.LG].

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Information capture and reuse strategies in Monte Carlo Tree Search, with applications to games of hidden information. Powley, E. J., Cowling, P. I. and Whitehouse, D. (2014) Artificial Intelligence, 217:92–116.

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Monte Carlo Tree Search Variants for Simultaneous Move Games. Tak, M. J. W., Lanctot, M. and Winands, M. H. M. (2014) 2014 IEEE Conference on Computational Intelligence and Games, pages 1–8.

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Solving Large Imperfect Information Games Using CFR+. Tammelin, O. (2014)

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Monte-Carlo Tree Search and minimax hybrids. Baier, H. and Winands, M. H. M. (2013) 2013 IEEE Conference on Computational Inteligence in Games (CIG), pages 1–8.

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Using Double-Oracle Method and Serialized Alpha-Beta Search for Pruning in Simultaneous Move Games. Bošanský, B., Lisý, V., Čermák, J., Vítek, R. and Pěchouček, M. (2013) Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pages 48–54.

[Burch 2013]

Solving Imperfect Information Games Using Decomposition. Burch, N., Johanson, M. B. and Bowling, M. (2013) AAAI Conference on Artificial Intelligence.

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Monte Carlo Tree Search for Simultaneous Move Games: A Case Study in the Game of Tron. Lancto, M., Wittlinger, C., Winands, M. H.M. and Den Teuling, N. G.P. (2013) Proceedings of the Twenty-Fifth Benelux Conference on Artificial Intelligence (BNAIC), pages 104–111.

[Lisý 2013]

Convergence of Monte Carlo Tree Search in Simultaneous Move Games. Lisý, V., Kovařík, V., Lanctot, M. and Bošanský, B. (2013) Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, pages 2112–2120.

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Decentralized Stochastic Control with Partial History Sharing: A Common Information Approach. Nayyar, A., Mahajan, A. and Teneketzis, D. (2013) Automatic Control, IEEE Transactions on, 58(7):1644–1658.

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A Survey of Monte Carlo Tree Search Methods. Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S. and Colton, S. (2012) IEEE Transactions on Computational Intelligence and AI in Games, 4(1):1–43.

[Burch 2012]

Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions. Burch, N., Lanctot, M., Szafron, D. and Gibson, R. (2012) Advances in Neural Information Processing Systems.

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Information Set Monte Carlo Tree Search. Cowling, P. I., Powley, E. J. and Whitehouse, D. (2012) IEEE Transactions on Computational Intelligence and AI in Games, 4(2):120–143.

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Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering. Cowling, P. I., Ward, C. D. and Powley, E. J. (2012) IEEE Transactions on Computational Intelligence and AI in Games, 4(4):241–257.

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Comparison of different selection strategies in Monte-Carlo Tree Search for the game of Tron. Perick, P., St-Pierre, D. L., Maes, F. and Ernst, D. (2012) 2012 IEEE Conference on Computational Intelligence and Games (CIG), pages 242–249.

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N-Grams and the Last-Good-Reply Policy Applied in General Game Playing. Tak, M. J. W., Winands, M. H. M. and Bjornsson, Y. (2012) IEEE Transactions on Computational Intelligence and AI in Games, 4(2):73–83.

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MCTS Based on Simple Regret. Tolpin, D. and Shimony, S. E. (2012) Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, pages 570–576.

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Ensemble Monte-Carlo Planning: An Empirical Study. Fern, A. and Lewis, P. (2011) Proceedings of the International Conference on Automated Planning and Scheduling, 21(1):58–65.

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Scalable distributed Monte-Carlo tree search. Yoshizoe, K., Kishimoto, A., Kaneko, T., Yoshimoto, H. and Ishikawa, Y. (2011) Proceedings of the 4th Annual Symposium on Combinatorial Search, SoCS 2011, pages 180–187.

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Understanding the Success of Perfect Information Monte Carlo Sampling in Game Tree Search. Long, J., Sturtevant, N., Buro, M. and Furtak, T. (2010) Proceedings of the AAAI Conference on Artificial Intelligence, 24(1):134–140.

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Massively Parallel Monte Carlo Tree Search. Rocki, K. and Suda, R. (2010)

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Symmetry Detection in General Game Playing. Schiffel, S. (2010) Proceedings of the AAAI Conference on Artificial Intelligence, 24(1):980–985.

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Lower Bounding Klondike Solitaire with Monte-Carlo Planning. Bjarnason, R., Fern, A. and Tadepalli, P. (2009) Proceedings of the International Conference on Automated Planning and Scheduling, 19(1):26–33.

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Monte Carlo Sampling for Regret Minimization in Extensive Games. Lanctot, M., Waugh, K., Zinkevich, M. and Bowling, M. (2009) Advances in Neural Information Processing Systems.

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Comparing UCT versus CFR in Simultaneous Games. Shafiei, M., Sturtevant, N. R. and Schaeffer, J. (2009)

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