好奇心駆動探索による地図なしナビゲーションの深層強化学習
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
深層強化学習に好奇心に基づく内的報酬を加え、地図情報なしで移動ロボットが目標へナビゲーションする方策を学習させる手法を提案し、未知環境への汎化性能が向上することを示した。
著者: Oleksii Zhelo, Jingwei Zhang, Lei Tai, Ming Liu, Wolfram Burgard
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal external reward for training DRL algorithms with intrinsic reward signals measured by curiosity. We test our approach in a mapless navigation setting, where the autonomous agent is required to navigate without the occupancy map of the environment, to targets whose relative locations can be easily acquired through low-cost solutions (e.g., visible light localization, Wi-Fi signal localization). We validate that the intrinsic motivation is crucial for improving DRL performance in tasks with challenging exploration requirements. Our experimental results show that our proposed method is able to more effectively learn navigation policies, and has better generalization capabilities in previously unseen environments. A video of our experimental results can be found at https://goo.gl/pWbpcF.
