強化学習による平流気球の自律航行
Marc G Bellemare1, Salvatore Candido2, Pablo Samuel Castro3
1Brain Team, Google Research, Montreal, Quebec, Canada. bellemare@google.com.
Nature
|December 3, 2020
まとめ
強化学習により,超圧気球の自動飛行制御が可能になり,データの不完全性を克服します. このAI駆動のシステムは 前の方法より効率的に ストラトスフィアをナビゲートします
科学分野:
- 人工知能
- 航空宇宙工学
- 自動運転システム
背景:
- ストラトスフィアの気球のナビゲーションは 風の変動,予測の誤り,データ不足などの課題に直面しています
- 常識的な制御方法は,ダイナミックな平流環境でリアルタイムで意思決定を行うには不十分です.
研究 の 目的:
- 強化学習を用いた超圧気球のための高性能フライトコントローラを開発する.
- 物理システムの不完全なデータで補強学習の課題に取り組むこと.
主な方法:
- 強化学習 (RL) を利用し,データ増強と自己訂正設計を行いました.
- RLコントローラをグローバルにローンの超圧気球に配置した.
- 太平洋上空で39日間の実験を行いました
主要な成果:
- RL コントローラーは Loon の以前のアルゴリズムと比較して優れたパフォーマンスを示しました.
- コントローラーは,様々な平流圏の風の条件に対して頑丈であることが証明されました.
- 不完全な現実データに RL を適用する障害を克服しました
結論:
- 強化学習は 複雑で現実的なシナリオにおける 自動制御のための 効果的な解決策を提供します
- このアプローチは,ダイナミックな環境との継続的な相互作用を必要とするシステムに適しています.
- 自律的なナビゲーションと制御のための 進歩したAIエージェントの道を開きます
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