Target-driven visual navigation in indoor scenes using reinforcement learning and imitation learning

[摘要] Here, the challenges of sample efficiency and navigation performance in deep reinforcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed. Our contributions are mainly three folds: first, a framework combining imitation learning with deep reinforcement learning is presented, which enables a robot to learn a stable navigation policy faster in the target-driven navigation task. Second, the surrounding images is taken as the observation instead of sequential images, which can improve the navigation performance for more information. Moreover, a simple yet efficient template matching method is adopted to determine the stop action, making the system more practical. Simulation experiments in the AI-THOR environment show that the proposed approach outperforms previous end-to-end deep reinforcement learning approaches, which demonstrate the effectiveness and efficiency of our approach.

[学科分类] 数学（综合）

[关键词] mobile robots;image matching;radionavigation;reinforcement learning