论文标题
从模拟到现实世界的学习掠食者捕食者的框架
A Framework for Learning Predator-prey Agents from Simulation to Real World
论文作者
论文摘要
在本文中，我们提出了一个进化的PredatorPrey机器人系统，通常可以从模拟到现实世界实施。我们将带相机和红外传感器的闭环机器人系统设计为控制器的输入。捕食者和猎物都通过增强拓扑（整洁）的神经进化来进化，以学习预期的行为。我们设计了一个框架，该框架集成了OpenAI，机器人操作系统（ROS），凉亭的体育馆。在这样的框架中，用户只需要专注于算法而不担心在模拟和现实世界中操纵机器人的细节。结合了模拟，现实世界的进化和鲁棒性分析，它可以应用于开发捕食者捕集任务的解决方案。为了方便用户，模拟和现实世界的源代码和视频在Github上发布。
In this paper, we propose an evolutionary predatorprey robot system which can be generally implemented from simulation to the real world. We design the closed-loop robot system with camera and infrared sensors as inputs of controller. Both the predators and prey are co-evolved by NeuroEvolution of Augmenting Topologies (NEAT) to learn the expected behaviours. We design a framework that integrate Gym of OpenAI, Robot Operating System (ROS), Gazebo. In such a framework, users only need to focus on algorithms without being worried about the detail of manipulating robots in both simulation and the real world. Combining simulations, real-world evolution, and robustness analysis, it can be applied to develop the solutions for the predator-prey tasks. For the convenience of users, the source code and videos of the simulated and real world are published on Github.
