Research press release



アルファスター(AlphaStar)という人工知能プログラムが、リアルタイム戦略ゲーム「スタークラフト2(StarCraft II)」の人間プレイヤーのランキングで上位0.2%に入った。アルファスターは、マルチエージェント強化学習アルゴリズムで、機械学習にとっての大きな成果とされており、これを応用することで、他のアプリケーションの複雑な問題の解決に役立つ可能性があるとするOriol Vinyalsたちの論文が、今週掲載される。



An artificial intelligence program called AlphaStar now ranks among the top 0.2% of human players for the real-time strategy game StarCraft II. Reported in this week’s Nature, the algorithm represents a major achievement for machine learning that could be adapted to help solve complex problems for other applications.

StarCraft II is a science fiction strategy game that is played professionally in competitions around the world; it is one of the world’s most lucrative professional electronic sports, or esports. In the game, players compete against one another, controlling one of three different alien races that each have different characteristics and abilities. The game has emerged as a grand challenge in the field of artificial intelligence research; previous artificial agents have failed to rival top human players, despite simplifying the game rules, manually programming certain action sequences or relying on superhuman capabilities such as executing tens of thousands of actions per minute.

Oriol Vinyals and colleagues present a multi-agent reinforcement learning algorithm called AlphaStar, in which several deep neural network agents compete against each other, generating a league of continually adapting strategies and counter-strategies. AlphaStar then competed against human players in a series of online games, where it reached Grandmaster level for all three of the StarCraft races. This makes it the first artificial agent to reach the top tier of human performance in a professionally played esport without simplifying the game.

doi: 10.1038/s41586-019-1724-z

「Nature 関連誌注目のハイライト」は、ネイチャー広報部門が報道関係者向けに作成したリリースを翻訳したものです。より正確かつ詳細な情報が必要な場合には、必ず原著論文をご覧ください。

メールマガジンリストの「Nature 関連誌今週のハイライト」にチェックをいれていただきますと、毎週最新のNature 関連誌のハイライトを皆様にお届けいたします。