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The Update-Equivalence Framework for Decision-Time Planning. Sokota, S., Farina, G., Wu, D. J., Hu, H., Wang, K. A., Kolter, J. Z. and Brown, N. (2024) arXiv:2304.13138 [cs.AI].

[Sokota 2023]

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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Monte Carlo Tree Search: a review of recent modifications and applications. Świechowski, M., Godlewski, K., Sawicki, B. and Mańdziuk, J. (2023) Artificial Intelligence Review, 56(3):2497–2562.

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[Diddigi 2022]

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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Rethinking formal models of partially observable multiagent decision making. Kovařík, V., Schmid, M., Burch, N., Bowling, M. and Lisý, V. (2022) Artificial Intelligence, 303:103645.

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A Survey of Opponent Modeling in Adversarial Domains. Nashed, S. and Zilberstein, S. (2022) Journal of Artificial Intelligence Research, 73.

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Mastering the game of Stratego with model-free multiagent reinforcement learning. Perolat, J., De Vylder, B., Hennes, D., Tarassov, E., Strub, F., de Boer, V., Muller, P., Connor, J. T., Burch, N., Anthony, T., McAleer, S., Elie, R., Cen, S. H., Wang, Z., Gruslys, A., Malysheva, A., Khan, M., Ozair, S., Timbers, F., Pohlen, T., Eccles, T., Rowland, M., Lanctot, M., Lespiau, J., Piot, B., Omidshafiei, S., Lockhart, E., Sifre, L., Beauguerlange, N., Munos, R., Silver, D., Singh, S., Hassabis, D. and Tuyls, K. (2022) Science, 378(6623):990–996.

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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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OLIVAW: Mastering Othello without Human Knowledge, nor a Fortune. Norelli, A. and Panconesi, A. (2021) arXiv:2103.17228 [cs.LG].

[Perolat 2021]

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.

[Saffidine 2021]

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.

[Schmid 2021]

Student of Games. Schmid, M., Moravčík, M., Burch, N., Kadlec, R., Davidson, J., Waugh, K., Bard, N., Timbers, F., Lanctot, M., Holland, Z., Davoodi, E., Christianson, A. and Bowling, M. H. (2021) arXiv:2112.03178 [cs.AI].

[Yang 2021]

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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[Gray 2020]

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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Limited lookahead in imperfect-information games. Kroer, C. and Sandholm, T. (2020) Artificial Intelligence, 283:103218.

[Lerer 2020]

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.

[O'Donoghue 2020]

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

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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)

[Bard 2019]

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.

[Brown 2019]

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

[Brown 2019b]

Solving Imperfect-Information Games via Discounted Regret Minimization. Brown, N. and Sandholm, T. (2019) Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence.

[Foerster 2019]

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].

[Goodman 2019]

Re-determinizing MCTS in Hanabi. Goodman, J. (2019) 2019 IEEE Conference on Games (CoG), pages 1–8.

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Recurrent Experience Replay in Distributed Reinforcement Learning. Kapturowski, S., Ostrovski, G., Dabney, W., Quan, J. and Munos, R. (2019) International Conference on Learning Representations.

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Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. Schrittwieser, J., Ioannis, A., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., Lillicrap, T. P. and Silver, D. (2019)

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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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Depth-Limited Solving for Imperfect-Information Games. Brown, N., Sandholm, T. and Amos, B. (2018) Advances in Neural Information Processing Systems, pages 7663–7674.

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Deep Counterfactual Regret Minimization. Brown, N., Lerer, A., Gross, S. and Sandholm, T. (2018) arXiv:1811.00164 [cs.AI].

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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)

[Russo 2018]

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)

[Silver 2018]

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].

[Xiao 2018]

Memory-Augmented Monte Carlo Tree Search. Xiao, C., Mei, J. and Müller, M. (2018) Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).

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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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Safe and Nested Subgame Solving for Imperfect-Information Games. Brown, N. and Sandholm, T. (2017) Neural Information Processing Systems.

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Noisy Networks for Exploration. Fortunato, M., Azar, M. G., Piot, B., Menick, J., Osband, I., Graves, A., Mnih, V., Munos, R., Hassabis, D., Pietquin, O., Blundell, C. and Legg, S. (2017) CoRR, abs/1706.10295.

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Thompson Sampling for Monte Carlo Tree Search and Maximin Action Identification. Li, M. (2017) Master's thesis, Universiteit Leiden, Leiden, Netherlands.

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[Moravčík 2017]

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.

[Neller 2017]

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

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Proximal Policy Optimization Algorithms. Schulman, J., Wolski, F., Dhariwal, P., Radford, A. and Klimov, O. (2017) CoRR, abs/1707.06347.

[Silver 2017]

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., van den Driessche, G., Graepel, T. and Hassabis, D. (2017) Nature, 550(7676):354–359.

[Svenstrup 2017]

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

[Volkert 2017]

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

[Baier 2016]

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.

[Mirsoleimani 2016]

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., van den 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.

[Bowling 2015]

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

[Clark 2015]

Training Deep Convolutional Neural Networks to Play Go. Clark, C. and Storkey, A. (2015) Proceedings of the 32nd International Conference on Machine Learning, pages 1766–1774.

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

[Lisý 2015]

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.

[Mnih 2015]

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].

[Tian 2015]

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].

[Powley 2014]

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.

[Tak 2014]

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.

[Baier 2013]

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.

[Lanctot 2013]

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.

[Nayyar 2013]

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, pages undefined.

[Cowling 2012]

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.

[Cowling 2012b]

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.

[Den Teuling 2012]

Monte-Carlo Tree Search for the Simultaneous Move Game Tron. Den Teuling, N. G.P. and Winands, M. H.M. (2012)

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No-Regret Learning in Extensive-Form Games with Imperfect Recall. Lanctot, M., Gibson, R., Burch, N., Zinkevich, M. and Bowling, M. (2012) arXiv:1205.0622 [cs.GT].

[Perick 2012]

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.

[Tak 2012]

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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Large-Scale Parallel Monte Carlo Tree Search on GPU. Rocki, K. and Suda, R. (2011) 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, pages 2034–2037.

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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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Enumeration of Nash Equilibria for Two-Player Games. Avis, D., Rosenberg, G. D., Savani, R. and von Stengel, B. (2010) Economic Theory, 42(1):9–37.

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A Lock-Free Multithreaded Monte-Carlo Tree Search Algorithm. Enzenberger, M. and Müller, M. (2010) Advances in Computer Games, pages 14–20.

[Long 2010]

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.

[Rocki 2010]

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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Monte-Carlo Tree Search Solver. Winands, M. H. M., Björnsson, Y. and Saito, J. (2008) Computers and Games, pages 25–36.

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Regret Minimization in Games with Incomplete Information. Zinkevich, M., Johanson, M., Bowling, M. and Piccione, C. (2007) Advances in Neural Information Processing Systems.

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