安全多智能体导航：基于目标条件安全强化学习
Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning
AI总结：
本文提出融合规划与安全强化学习的方法，通过目标条件策略和图构建实现多智能体安全导航，利用冲突搜索生成路点计划，在复杂环境中有效平衡安全与效率。
摘要：
安全导航对于在危险环境中运行的自主系统至关重要。传统规划方法擅长处理长时域任务，但依赖于具有固定距离度量的预定义图。相比之下，安全强化学习（RL）无需依赖手动启发式规则即可学习复杂行为，但无法解决长时域任务，尤其是在目标条件和多智能体场景中。\n在本文中，我们提出了一种新颖的方法，融合了规划和安全RL的优势。我们的方法利用目标条件RL和安全RL来学习用于导航的目标条件策略，同时通过自动自训练算法使用学习的价值函数来估计累积距离和安全水平。通过使用回放缓冲区中的状态构建图，我们的方法剪除不安全的边，并生成基于路点的计划，智能体遵循该计划直至到达目标，从而在长距离上有效平衡更快和更安全的路线。\n利用这种统一的高层图和一个共享的低层目标条件安全RL策略，我们将该方法扩展到解决多智能体安全导航问题。特别是，我们利用冲突搜索（CBS）为多个智能体创建基于路点的计划，使其能够在扩展时域内安全导航。这种集成增强了目标条件安全RL在多智能体场景中的可扩展性，实现了智能体之间的高效协调。\n与最先进基线的广泛基准测试证明了我们的方法在复杂和危险环境中安全实现多个智能体距离目标的有效性。我们的代码和更多工作细节可在 https://safe-visual-mapf-mers.csail.mit.edu/ 获取。
英文摘要：
Safe navigation is essential for autonomous systems operating in hazardous environments. Traditional planning methods excel at long-horizon tasks but rely on a predefined graph with fixed distance metrics. In contrast, safe Reinforcement Learning (RL) can learn complex behaviors without relying on manual heuristics but fails to solve long-horizon tasks, particularly in goal-conditioned and multi-agent scenarios. In this paper, we introduce a novel method that integrates the strengths of both planning and safe RL. Our method leverages goal-conditioned RL and safe RL to learn a goal-conditioned policy for navigation while concurrently estimating cumulative distance and safety levels using learned value functions via an automated self-training algorithm. By constructing a graph with states from the replay buffer, our method prunes unsafe edges and generates a waypoint-based plan that the agent follows until reaching its goal, effectively balancing faster and safer routes over extended distances. Utilizing this unified high-level graph and a shared low-level goal-conditioned safe RL policy, we extend this approach to address the multi-agent safe navigation problem. In particular, we leverage Conflict-Based Search (CBS) to create waypoint-based plans for multiple agents allowing for their safe navigation over extended horizons. This integration enhances the scalability of goal-conditioned safe RL in multi-agent scenarios, enabling efficient coordination among agents. Extensive benchmarking against state-of-the-art baselines demonstrates the effectiveness of our method in achieving distance goals safely for multiple agents in complex and hazardous environments. Our code and further details about or work is available at https://safe-visual-mapf-mers.csail.mit.edu/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
