深層強化学習によるUAVの時変フォーメーション制御
Time-Varying Formation Controllers for Unmanned Aerial Vehicles Using Deep Reinforcement Learning
深層強化学習をマルチエージェント的に用い、UAV群が最適性と移植性を保ちながら任意の時変フォーメーションに素早く到達する制御器を設計した。
著者: Ronny Conde, José Ramón Llata, Carlos Torre-Ferrero
分類: cs.RO
原文アブストラクト
We consider the problem of designing scalable and portable controllers for unmanned aerial vehicles (UAVs) to reach time-varying formations as quickly as possible. This brief confirms that deep reinforcement learning can be used in a multi-agent fashion to drive UAVs to reach any formation while taking into account optimality and portability. We use a deep neural network to estimate how good a state is, so the agent can choose actions accordingly. The system is tested with different non-high-dimensional sensory inputs without any change in the neural network architecture, algorithm or hyperparameters, just with additional training.