介入支援強化学習による安全で実用的なナビゲーション方策最適化
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
人間の介入を活用してロボットと環境の相互作用から方策を改善するIARLフレームワークを提案し、UAVの自律ナビゲーションで安全性を保ちつつ介入回数を削減し性能を向上させた。
著者: Fan Wang, Bo Zhou, Ke Chen, Tingxiang Fan, Xi Zhang, Jiangyong Li, Hao Tian, Jia Pan
分類: cs.RO, cs.AI
原文アブストラクト
Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety and cost in practical applications. In this paper, we propose the Intervention Aided Reinforcement Learning (IARL) framework, which utilizes human intervened robot-environment interaction to improve the policy. We used the Unmanned Aerial Vehicle (UAV) as the test platform. We built neural networks as our policy to map sensor readings to control signals on the UAV. Our experiment scenarios cover both simulation and reality. We show that our approach substantially reduces the human intervention and improves the performance in autonomous navigation, at the same time it ensures safety and keeps training cost acceptable.
