AlphaZero

In late 2017 we introduced AlphaZero, a single system that taught itself from scratch how to master the games of chess, shogi (Japanese chess), and Go, beating a world-champion program in each case. We were excited by the preliminary results and thrilled to see the response from members of the chess community, who saw in AlphaZero’s games a ground-breaking, highly dynamic and “unconventional” style of play that differed from any chess playing engine that came before it.


References in zbMATH (referenced in 29 articles , 1 standard article )

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  1. Schnaubelt, Matthias: Deep reinforcement learning for the optimal placement of cryptocurrency limit orders (2022)
  2. Cao, Yongcan; Zhan, Huixin: Efficient multi-objective reinforcement learning via multiple-gradient descent with iteratively discovered weight-vector sets (2021)
  3. de Nijs, Frits; Walraven, Erwin; de Weerdt, Mathijs M.; Spaan, Matthijs T. J.: Constrained multiagent Markov decision processes: a taxonomy of problems and algorithms (2021)
  4. Evans, Richard; Hernández-Orallo, José; Welbl, Johannes; Kohli, Pushmeet; Sergot, Marek: Making sense of sensory input (2021)
  5. Friston, Karl; Da Costa, Lancelot; Hafner, Danijar; Hesp, Casper; Parr, Thomas: Sophisticated inference (2021)
  6. Fujita, Yasuhiro; Nagarajan, Prabhat; Kataoka, Toshiki; Ishikawa, Takahiro: ChainerRL: a deep reinforcement learning library (2021)
  7. Furelos-Blanco, Daniel; Law, Mark; Jonsson, Anders; Broda, Krysia; Russo, Alessandra: Induction and exploitation of subgoal automata for reinforcement learning (2021)
  8. Mazyavkina, Nina; Sviridov, Sergey; Ivanov, Sergei; Burnaev, Evgeny: Reinforcement learning for combinatorial optimization: a survey (2021)
  9. Patra, Sunandita; Mason, James; Ghallab, Malik; Nau, Dana; Traverso, Paolo: Deliberative acting, planning and learning with hierarchical operational models (2021)
  10. Petersen, Philipp; Raslan, Mones; Voigtlaender, Felix: Topological properties of the set of functions generated by neural networks of fixed size (2021)
  11. Sanjuán, Miguel A. F.: Artificial intelligence, chaos, prediction and understanding in science (2021)
  12. Silva, Cleyton R.; Bowling, Michael; Lelis, Levi H. S.: Teaching people by justifying tree search decisions: an empirical study in curling (2021)
  13. Silver, David; Singh, Satinder; Precup, Doina; Sutton, Richard S.: Reward is enough (2021)
  14. Skirzyński, Julian; Becker, Frederic; Lieder, Falk: Automatic discovery of interpretable planning strategies (2021)
  15. Wang, Kun; Sun, WaiChing; Du, Qiang: A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks (2021)
  16. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  17. Bard, Nolan; Foerster, Jakob N.; Chandar, Sarath; Burch, Neil; Lanctot, Marc; Song, H. Francis; Parisotto, Emilio; Dumoulin, Vincent; Moitra, Subhodeep; Hughes, Edward; Dunning, Iain; Mourad, Shibl; Larochelle, Hugo; Bellemare, Marc G.; Bowling, Michael: The Hanabi challenge: a new frontier for AI research (2020)
  18. Cropper, Andrew; Evans, Richard; Law, Mark: Inductive general game playing (2020)
  19. Han, The Anh; Pereira, Luis Moniz; Santos, Francisco C.; Lenaerts, Tom: To regulate or not: a social dynamics analysis of an idealised AI race (2020)
  20. Liu, Yu; Chen, Yiming; Jiang, Tao: Dynamic selective maintenance optimization for multi-state systems over a finite horizon: a deep reinforcement learning approach (2020)

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