StarCraftのAI • Santiago Ontañon, Gabriel Synnaeve, Alberto Uriarte, Florian Richoux, David Churchill, et al.. • “A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft”. IEEE Transactions on Computational Intelligence and AI in games, IEEE Computational Intelligence Society, 2013, 5(4), pp.1-19. hal- 00871001 • https://hal.archives-ouvertes.fr/hal-00871001
StarCraft~StarCraft2における 人工知能 （DeepMind, 2019） Oriol Vinyals, et al., “StarCraft II: A New Challenge for Reinforcement Learning”, https://arxiv.org/abs/1708.04782 PySC2 - StarCraft II Learning Environment https://github.com/deepmind/pysc2
Deep Q-Learning (2013) Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller (DeepMind Technologies) Playing Atari with Deep Reinforcement Learning http://www.cs.toronto.edu/~vmnih/docs/dqn.pdf 画面を入力 操作はあらかじめ教える スコアによる強化学習
学習過程解析 Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller (DeepMind Technologies) Playing Atari with Deep Reinforcement Learning http://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
• Pπ ロールアウトポリシー（ロールアウトで討つ手を決める。 Pπ（a|s） sという状態でaを討つ確率） • Pσ Supervised Learning Network プロの討つ手からその 手を討つ確率を決める。Pσ（a|s）sという状態でaを討つ確 率。 • Pρ 強化学習ネットワーク。Pρ（学習済み）に初期化。 • Vθ(s’) 局面の状態 S’ を見たときに、勝敗の確率を予測 する関数。つまり、勝つか、負けるかを返します。 Mastering the game of Go with deep neural networks and tree search http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html https://deepmind.com/research/alphago/
囲碁AI： 位置評価関数から位置評価ニューラルネットワークへ Mastering the game of Go with deep neural networks and tree search http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html https://deepmind.com/research/alphago/ S Q R
Early in the learning process … … after 15 minutes of learning Reward for decrease in Wulong Goth’s health Ralf Herbrich, Thore Graepel, Joaquin Quiñonero Candela Applied Games Group,Microsoft Research Cambridge "Forza, Halo, Xbox Live The Magic of Research in Microsoft Products" http://research.microsoft.com/en-us/projects/drivatar/ukstudentday.pptx
Early in the learning process … … after 15 minutes of learning Punishment for decrease in either player’s health Ralf Herbrich, Thore Graepel, Joaquin Quiñonero Candela Applied Games Group,Microsoft Research Cambridge "Forza, Halo, Xbox Live The Magic of Research in Microsoft Products" http://research.microsoft.com/en-us/projects/drivatar/ukstudentday.pptx
参考文献 • A. Summerville, S. Snodgrass, M. Guzdial, C. Holmgård, A. K. Hoover, A. Isaksen, A. Nealen , J. Togelius, Procedural Content Generation via Machine Learning (PCGML), 2018. • M. Guzdial, S. Snodgrass , A. J. Summerville, Procedural Content Generation via Machine Learning: An Overview, Springer, 2022. • A. Summerville , M. Mateas, “Super Mario as a String: Platformer Level Generation Via LSTMs,” Proceedings of 1st International Joint Conference of DiGRA and FDG, http://www.digra.org/wp- content/uploads/digital-library/paper_129.pdf, 2016.
PCGRL (Procedural Contents Generation via Reinforcement Leaning) PCGRL: Procedural Content Generation via Reinforcement Learning Ahmed Khalifa, Philip Bontrager, Sam Earle, Julian Togelius https://arxiv.org/abs/2001.09212
Deep Reinforcement Learning for Navigation in AAA Video Games https://montreal.ubisoft.com/en/deep-reinforcement-learning-for-navigation-in-aaa- video-games/
Deep Reinforcement Learning for Navigation in AAA Video Games https://montreal.ubisoft.com/en/deep-reinforcement-learning-for-navigation-in-aaa- video-games/
手法 (1)状態：３次元占有マップと２次元深度マップを取る (2)強化学習する (3)ランダムにエージェント・シリンダーを生成して学習 Deep Reinforcement Learning for Navigation in AAA Video Games https://montreal.ubisoft.com/en/deep-reinforcement-learning-for-navigation-in-aaa- video-games/
Deep Reinforcement Learning for Navigation in AAA Video Games https://montreal.ubisoft.com/en/deep-reinforcement-learning-for-navigation-in-aaa-video-games/
Deep Reinforcement Learning for Navigation in AAA Video Games https://montreal.ubisoft.com/en/deep-reinforcement-learning-for-navigation-in-aaa-video-games/
ゲームデザイナーを 助けるAI NeurIPS 2022: Imitation Learning to Inform the Design of Computer Games https://www.ea.com/seed/news/imitation-learning-design-validation-games
• プレイヤーの行動を模倣する人工知能 NeurIPS 2022: Imitation Learning to Inform the Design of Computer Games https://www.ea.com/seed/news/imitation-learning-design-validation-games ゲームデザイナーを助けるAI
ゲームデザイナーの作ったレベルを 自動テストするAI NeurIPS 2022: Imitation Learning to Inform the Design of Computer Games https://www.ea.com/seed/news/imitation-learning-design-validation-games
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