论文标题
内部注意力支持基于多机构增强学习的异质多机器人组合的自适应合作
Inner Attention Supported Adaptive Cooperation for Heterogeneous Multi Robots Teaming based on Multi-agent Reinforcement Learning
论文作者
论文摘要
人类可以根据不同的任务要求，其他人的能力和可用性有选择地关注不同的信息。因此，它们可以迅速适应完全不同且复杂的环境。如果像人一样，机器人可以获得相同的能力，那么它将大大提高其对新的和意外情况的适应性。异类多机器人组合的最新努力试图实现此能力，例如基于通信和多模式信息融合策略的方法。但是，这些方法不仅会因机器人数量的增加而遭受指数爆炸问题，而且还需要大量的计算资源。为此，我们引入了一种内在注意力参与者批评方法，该方法复制了人类灵活合作的各个方面。通过将自然语言过程的注意机制带入多机器人合作的领域，我们的注意力方法能够动态选择要参加哪些机器人。为了测试我们提出的方法的有效性，已经设计了一些模拟实验。结果表明，内部注意机制可以实现灵活的合作，并降低拯救任务中消耗的资源。
Humans can selectively focus on different information based on different tasks requirements, other people's abilities and availability. Therefore, they can adapt quickly to a completely different and complex environments. If, like people, robot could obtain the same abilities, then it would greatly increase their adaptability to new and unexpected situations. Recent efforts in Heterogeneous Multi Robots Teaming have try to achieve this ability, such as the methods based on communication and multi-modal information fusion strategies. However, these methods will not only suffer from the exponential explosion problem with the increase of robots number but also need huge computational resources. To that end, we introduce an inner attention actor-critic method that replicates aspects of human flexibly cooperation. By bringing attention mechanism on computer vision, natural language process into the realm of multi-robot cooperation, our attention method is able to dynamically select which robots to attend to. In order to test the effectiveness of our proposed method, several simulation experiments have been designed. And the results show that inner attention mechanism can enable flexible cooperation and lower resources consuming in rescuing tasks.
