自己注意機構を用いた解釈可能な深層強化学習によるUAV衝突回避
Interpretable UAV Collision Avoidance using Deep Reinforcement Learning
自己注意機構を組み込んだ深層強化学習で、未知の環境や天候変化にも頑健に動作し、判断根拠も解釈できるマルチロータUAVの衝突回避アルゴリズムを提案した。
著者: Deepak-George Thomas, Daniil Olshanskyi, Karter Krueger, Tichakorn Wongpiromsarn, Ali Jannesari
分類: cs.RO, cs.LG
原文アブストラクト
The significant components of any successful autonomous flight system are task completion and collision avoidance. Most deep learning algorithms successfully execute these aspects under the environment and conditions they are trained. However, they fail when subjected to novel environments. This paper presents an autonomous multi-rotor flight algorithm, using Deep Reinforcement Learning augmented with Self-Attention Models, that can effectively reason when subjected to varying inputs. In addition to their reasoning ability, they are also interpretable, enabling it to be used under real-world conditions. We have tested our algorithm under different weather conditions and environments and found it robust compared to conventional Deep Reinforcement Learning algorithms.
