将图结构规划与强化学习结合，提升复杂环境下的导航探索能力。
Bridging Deep Reinforcement Learning and Motion Planning for Model-Free Navigation in Cluttered Environments
- 用全状态空间的图结构生成密集奖励信号
- 在复杂环境中探索效率提升，任务成功率显著提高
- 适合需要高效探索的机器人导航场景
深度强化学习（DRL）已成为学习最优策略的强大无模型范式。然而，在障碍物密集的导航任务中，DRL方法常因探索不足而表现不佳，尤其是在稀疏奖励或存在系统扰动的复杂动态环境下。为解决此问题，本文将通用的基于图的运动规划与DRL相结合，使智能体能够更有效地探索复杂空间，并实现预期的导航性能。具体而言，我们设计了一种基于完整状态空间图结构的密集奖励函数，该图提供丰富引导，帮助智能体趋向最优策略。我们在具有挑战性的环境中验证了该方法，结果表明其在探索效率和任务成功率方面均有显著提升。
原文摘要 · Abstract (English)
Deep Reinforcement Learning (DRL) has emerged as a powerful model-free paradigm for learning optimal policies. However, in navigation tasks with cluttered environments, DRL methods often suffer from insufficient exploration, especially under sparse rewards or complex dynamics with system disturbances. To address this challenge, we bridge general graph-based motion planning with DRL, enabling agents to explore cluttered spaces more effectively and achieve desired navigation performance. Specifically, we design a dense reward function grounded in a graph structure that spans the entire state space. This graph provides rich guidance, steering the agent toward optimal strategies. We validate our approach in challenging environments, demonstrating substantial improvements in exploration efficiency and task success rates.
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