用拓扑地图和宏观动作，让DQN高效导航复杂环境。
Deep Reinforcement Learning Based Navigation with Macro Actions and Topological Maps
- 基于物体检测构建拓扑地图，用宏观动作替代细粒度控制。
- 在真实3D环境中，性能显著优于随机基线，支持即时与最终奖励。
- 适合研究样本效率高、泛化能力强的强化学习导航方法者。
本文解决在大型视觉复杂环境中稀疏奖励下的导航难题。提出一种基于对象的宏观动作方法，结合拓扑地图，使简单Deep Q-Network（DQN）能学习有效导航策略。智能体通过RGBD输入检测物体并选择对应导航的离散宏观动作，该抽象大幅降低强化学习问题复杂度，并实现对未见环境的泛化。我们在一个逼真的3D仿真环境中评估该方法，结果表明其在即时奖励和最终奖励条件下均显著优于随机基线。结果证明，拓扑结构与宏观抽象可实现仅从像素数据出发的样本高效学习。
原文摘要 · Abstract (English)
This paper addresses the challenge of navigation in large, visually complex environments with sparse rewards. We propose a method that uses object-oriented macro actions grounded in a topological map, allowing a simple Deep Q-Network (DQN) to learn effective navigation policies. The agent builds a map by detecting objects from RGBD input and selecting discrete macro actions that correspond to navigating to these objects. This abstraction drastically reduces the complexity of the underlying reinforcement learning problem and enables generalization to unseen environments. We evaluate our approach in a photorealistic 3D simulation and show that it significantly outperforms a random baseline under both immediate and terminal reward conditions. Our results demonstrate that topological structure and macro-level abstraction can enable sample-efficient learning even from pixel data.
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