Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning-猫眼课题宝

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发表时间：

2018-04

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ArXiv

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其他

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作者：

Oleksii Zhelo;Jingwei Zhang;L. Tai;Ming Liu-;Wolfram Burgard

研究了深度强化学习(DRL)方法学习移动机器人导航策略的探索策略。特别是，我们用由好奇心衡量的内在奖励信号来增加训练DRL算法的正常外部奖励。我们在无地址导航环境中测试了我们的方法，在这种情况下，自主代理需要在没有环境占用地图的情况下导航到目标，这些目标的相对位置可以通过低成本的解决方案(例如可见光定位、Wi-Fi信号定位)轻松获得。我们证实，在具有挑战性的探索需求的任务中，内在动机对于提高DRL性能至关重要。实验结果表明，该方法能够更有效地学习导航策略，并且在未知环境下具有更好的泛化能力。我们的实验结果的视频可在以下HTTPS URL中找到

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 this https URL

Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning

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