学习在障碍物中放牧代理：使用深度强化学习训练鲁棒的牧羊行为
Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors using Deep Reinforcement Learning
摘要：
机器人牧羊问题考虑的是一组相干代理的控制和导航（例如，一群鸟或一队无人机）通过外部机器人的运动，称为牧羊人。基于机器学习的方法已经成功地在没有障碍物的空旷环境中解决了这个问题。另一方面，基于规则的方法可以处理环境中充满障碍物的更复杂的场景，并允许多个牧羊人协同工作。然而，这些基于规则的方法是脆弱的，因为很难定义一套全面的规则，可以处理所有可能的情况。为了克服这些局限性，我们提出了第一个已知的基于学习的方法，可以在障碍物之间放牧代理。通过使用深度强化学习技术与概率路线图相结合，我们使用嘈杂但受控的环境和行为参数训练牧羊模型。我们的实验结果表明，该方法是鲁棒的，即它是不敏感的不确定性来源于环境和行为模型。因此，该方法具有更高的成功率，更短的完成时间和路径长度比基于规则的行为方法。这些优势在涉及更困难的群体和艰苦的通道的更具挑战性的场景中尤为突出。
英文摘要：
Robotic shepherding problem considers the control and navigation of a group of coherent agents (e.g., a flock of bird or a fleet of drones) through the motion of an external robot, called shepherd. Machine learning based methods have successfully solved this problem in an empty environment with no obstacles. Rule-based methods, on the other hand, can handle more complex scenarios in which environments are cluttered with obstacles and allow multiple shepherds to work collaboratively. However, these rule-based methods are fragile due to the difficulty in defining a comprehensive set of rules that can handle all possible cases. To overcome these limitations, we propose the first known learning-based method that can herd agents amongst obstacles. By using deep reinforcement learning techniques combined with the probabilistic roadmaps, we train a shepherding model using noisy but controlled environmental and behavioral parameters. Our experimental results show that the proposed method is robust, namely, it is insensitive to the uncertainties originated from both environmental and behavioral models. Consequently, the proposed method has a higher success rate, shorter completion time and path length than the rule-based behavioral methods have. These advantages are particularly prominent in more challenging scenarios involving more difficult groups and strenuous passages.
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