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Mastering Atari, Go, chess and shogi by planning with a learned model - Nature.com

  • 1.

    Campbell, M., Hoane, A. J. Jr & Hsu, F.-h. Deep Blue. Artif. Intell. 134, 57–83 (2002).

    Article  Google Scholar 

  • 2.

    Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).

    ADS  CAS  Article  Google Scholar 

  • 3.

    Bellemare, M. G., Naddaf, Y., Veness, J. & Bowling, M. The arcade learning environment: an evaluation platform for general agents. J. Artif. Intell. Res. 47, 253–279 (2013).

    Article  Google Scholar 

  • 4.

    Machado, M. et al. Revisiting the arcade learning environment: evaluation protocols and open problems for general agents. J. Artif. Intell. Res. 61, 523–562 (2018).

    MathSciNet  Article  Google Scholar 

  • 5.

    Silver, D. et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362, 1140–1144 (2018).

    ADS  MathSciNet  CAS  Article  Google Scholar 

  • 6.

    Schaeffer, J. et al. A world championship caliber checkers program. Artif. Intell. 53, 273–289 (1992).

    Article  Google Scholar 

  • 7.

    Brown, N. & Sandholm, T. Superhuman AI for heads-up no-limit poker: Libratus beats top professionals. Science 359, 418–424 (2018).

    ADS  MathSciNet  CAS  Article  Google Scholar 

  • 8.

    Moravčík, M. et al. Deepstack: expert-level artificial intelligence in heads-up no-limit poker. Science 356, 508–513 (2017).

    ADS  MathSciNet  Article  Google Scholar 

  • 9.

    Vlahavas, I. & Refanidis, I. Planning and Scheduling Technical Report (EETN, 2013).

  • 10.

    Segler, M. H., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018).

    ADS  CAS  Article  Google Scholar 

  • 11.

    Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction 2nd edn (MIT Press, 2018).

  • 12.

    Deisenroth, M. & Rasmussen, C. PILCO: a model-based and data-efficient approach to policy search. In Proc. 28th International Conference on Machine Learning, ICML 2011 465–472 (Omnipress, 2011).

  • 13.

    Heess, N. et al. Learning continuous control policies by stochastic value gradients. In NIPS’15: Proc. 28th International Conference on Neural Information Processing Systems Vol. 2 (eds Cortes, C. et al.) 2944–2952 (MIT Press, 2015).

  • 14.

    Levine, S. & Abbeel, P. Learning neural network policies with guided policy search under unknown dynamics. Adv. Neural Inf. Process. Syst. 27, 1071–1079 (2014).

    Google Scholar 

  • 15.

    Hafner, D. et al. Learning latent dynamics for planning from pixels. Preprint at https://arxiv.org/abs/1811.04551 (2018).

  • 16.

    Kaiser, L. et al. Model-based reinforcement learning for atari. Preprint at https://arxiv.org/abs/1903.00374 (2019).

  • 17.

    Buesing, L. et al. Learning and querying fast generative models for reinforcement learning. Preprint at https://arxiv.org/abs/1802.03006 (2018).

  • 18.

    Espeholt, L. et al. IMPALA: scalable distributed deep-RL with importance weighted actor-learner architectures. In Proc. International Conference on Machine Learning, ICML Vol. 80 (eds Dy, J. & Krause, A.) 1407–1416 (2018).

  • 19.

    Kapturowski, S., Ostrovski, G., Dabney, W., Quan, J. & Munos, R. Recurrent experience replay in distributed reinforcement learning. In International Conference on Learning Representations (2019).

  • 20.

    Horgan, D. et al. Distributed prioritized experience replay. In International Conference on Learning Representations (2018).

  • 21.

    Puterman, M. L. Markov Decision Processes: Discrete Stochastic Dynamic Programming 1st edn (John Wiley & Sons, 1994).

  • 22.

    Coulom, R. Efficient selectivity and backup operators in Monte-Carlo tree search. In International Conference on Computers and Games 72–83 (Springer, 2006).

  • 23.

    Wahlström, N., Schön, T. B. & Deisenroth, M. P. From pixels to torques: policy learning with deep dynamical models. Preprint at http://arxiv.org/abs/1502.02251 (2015).

  • 24.

    Watter, M., Springenberg, J. T., Boedecker, J. & Riedmiller, M. Embed to control: a locally linear latent dynamics model for control from raw images. In NIPS’15: Proc. 28th International Conference on Neural Information Processing Systems Vol. 2 (eds Cortes, C. et al.) 2746–2754 (MIT Press, 2015).

  • 25.

    Ha, D. & Schmidhuber, J. Recurrent world models facilitate policy evolution. In NIPS’18: Proc. 32nd International Conference on Neural Information Processing Systems (eds Bengio, S. et al.) 2455–2467 (Curran Associates, 2018).

  • 26.

    Gelada, C., Kumar, S., Buckman, J., Nachum, O. & Bellemare, M. G. DeepMDP: learning continuous latent space models for representation learning. Proc. 36th International Conference on Machine Learning: Volume 97 of Proc. Machine Learning Research (eds Chaudhuri, K. & Salakhutdinov, R.) 2170–2179 (PMLR, 2019).

  • 27.

    van Hasselt, H., Hessel, M. & Aslanides, J. When to use parametric models in reinforcement learning? Preprint at https://arxiv.org/abs/1906.05243 (2019).

  • 28.

    Tamar, A., Wu, Y., Thomas, G., Levine, S. & Abbeel, P. Value iteration networks. Adv. Neural Inf. Process. Syst. 29, 2154–2162 (2016).

    Google Scholar 

  • 29.

    Silver, D. et al. The predictron: end-to-end learning and planning. In Proc. 34th International Conference on Machine Learning Vol. 70 (eds Precup, D. & Teh, Y. W.) 3191–3199 (JMLR, 2017).

  • 30.

    Farahmand, A. M., Barreto, A. & Nikovski, D. Value-aware loss function for model-based reinforcement learning. In Proc. 20th International Conference on Artificial Intelligence and Statistics: Volume 54 of Proc. Machine Learning Research (eds Singh, A. & Zhu, J) 1486–1494 (PMLR, 2017).

  • 31.

    Farahmand, A. Iterative value-aware model learning. Adv. Neural Inf. Process. Syst. 31, 9090–9101 (2018).

    Google Scholar 

  • 32.

    Farquhar, G., Rocktaeschel, T., Igl, M. & Whiteson, S. TreeQN and ATreeC: differentiable tree planning for deep reinforcement learning. In International Conference on Learning Representations (2018).

  • 33.

    Oh, J., Singh, S. & Lee, H. Value prediction network. Adv. Neural Inf. Process. Syst. 30, 6118–6128 (2017).

    Google Scholar 

  • 34.

    Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012).

    Google Scholar 

  • 35.

    He, K., Zhang, X., Ren, S. & Sun, J. Identity mappings in deep residual networks. In 14th European Conference on Computer Vision 630–645 (2016).

  • 36.

    Hessel, M. et al. Rainbow: combining improvements in deep reinforcement learning. In Thirty-Second AAAI Conference on Artificial Intelligence (2018).

  • 37.

    Schmitt, S., Hessel, M. & Simonyan, K. Off-policy actor-critic with shared experience replay. Preprint at https://arxiv.org/abs/1909.11583 (2019).

  • 38.

    Azizzadenesheli, K. et al. Surprising negative results for generative adversarial tree search. Preprint at http://arxiv.org/abs/1806.05780 (2018).

  • 39.

    Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).

    ADS  CAS  Article  Google Scholar 

  • 40.

    Open, A. I. OpenAI five. OpenAI https://blog.openai.com/openai-five/ (2018).

  • 41.

    Vinyals, O. et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature 575, 350–354 (2019).

    ADS  CAS  Article  Google Scholar 

  • 42.

    Jaderberg, M. et al. Reinforcement learning with unsupervised auxiliary tasks. Preprint at https://arxiv.org/abs/1611.05397 (2016).

  • 43.

    Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).

    ADS  CAS  Article  Google Scholar 

  • 44.

    Kocsis, L. & Szepesvári, C. Bandit based Monte-Carlo planning. In European Conference on Machine Learning 282–293 (Springer, 2006).

  • 45.

    Rosin, C. D. Multi-armed bandits with episode context. Ann. Math. Artif. Intell. 61, 203–230 (2011).

    MathSciNet  Article  Google Scholar 

  • 46.

    Schadd, M. P., Winands, M. H., Van Den Herik, H. J., Chaslot, G. M.-B. & Uiterwijk, J. W. Single-player Monte-Carlo tree search. In International Conference on Computers and Games 1–12 (Springer, 2008).

  • 47.

    Pohlen, T. et al. Observe and look further: achieving consistent performance on Atari. Preprint at https://arxiv.org/abs/1805.11593 (2018).

  • 48.

    Schaul, T., Quan, J., Antonoglou, I. & Silver, D. Prioritized experience replay. In International Conference on Learning Representations (2016).

  • 49.

    Cloud TPU. Google Cloud https://cloud.google.com/tpu/ (2019).

  • 50.

    Coulom, R. Whole-history rating: a Bayesian rating system for players of time-varying strength. In International Conference on Computers and Games 113–124 (2008).

  • 51.

    Nair, A. et al. Massively parallel methods for deep reinforcement learning. Preprint at https://arxiv.org/abs/1507.04296 (2015).

  • 52.

    Lanctot, M. et al. OpenSpiel: a framework for reinforcement learning in games. Preprint at http://arxiv.org/abs/1908.09453 (2019).

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