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深いニューラルネットワークとツリー検索でゴーゲームをマスターする
David Silver1, Aja Huang1, Chris J Maddison1
1Google DeepMind, 5 New Street Square, London EC4A 3TW, UK.
Nature
|January 29, 2016
まとめ
人工知能 (AI) は ディープニューラルネットワークを使って ゴルフに優れています AlphaGoは 監督学習と補強学習を組み合わせて 他のプログラムと比べて 99.8%の勝利率を達成しています
科学分野:
- 人工知能
- コンピュータ科学
- ゲーム理論
背景:
- Goゲームは広大な検索スペースと 複雑な動きの評価により AIにとって大きな課題となっています
- 伝統的なAIアプローチは ゴルフの複雑さと格闘し 専門家レベルのプレーを 長期的な目標にしています
研究 の 目的:
- ディープニューラルネットワークを用いた ゴルフのための新しいAIアプローチを開発する.
- 先進的な機械学習技術を通して Go のプロフェッショナルレベルのパフォーマンスを達成します.
主な方法:
- 役員陣の位置評価のための"価値ネットワーク"と,移転選択のための"政策ネットワーク"の導入
- ディープニューラルネットワークの訓練は,人間のエキスパートゲームによる監督学習と,自己プレイによる強化学習の組み合わせを用いて行われます.
- 価値と政策のネットワークとモンテカルロシミュレーションを統合した新しい検索アルゴリズムの開発
主要な成果:
- ニューラルネットワークだけで,ルックヘッド検索なしで,最先端のモンテカルロツリー検索プログラムに匹敵するパフォーマンスを達成しました.
- 新しい検索アルゴリズムを利用したAlphaGoプログラムは,他のGoプログラムに対して99.8%の勝利率を示しました.
- アルファゴーは人間のヨーロッパゴーチャンピオンを5-0で倒し AI能力の重要なマイルストーンとなりました
結論:
- 学習方法の新しい組み合わせで 訓練された深いニューラルネットワークは ゴルフのような複雑な戦略ゲームで 超人的なパフォーマンスを発揮できます
- この研究は,ゲームにおける人工知能の長年の課題を克服し,戦略的領域におけるAIの新しいパラダイムを示しています.
- アルファゴーの成功は 人工知能の研究で長い間求められてきた目標を達成し,他の複雑な意思決定分野にも潜在的に影響を与える大きな進歩を意味します.
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