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

Abstract

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.