论文标题
PIC4RL-GYM：一个用于机器人的ROS2模块化框架自动导航，并进行深度强化学习
PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning
论文作者
论文摘要
学习代理可以通过采用多种方法来优化标准自主导航，从而提高系统的灵活性，效率和计算成本。这项工作介绍了\ textIt {pic4rl-gym}，这是一个基本的模块化框架，可通过将ROS2和Guazebo（Ros2和Guazebo）与深度加固学习（DRL）相混合来增强导航和学习研究。本文描述了PIC4RL-GYM的整个结构，该结构完全集成了DRL代理在室内和室外导航方案和任务中的培训和测试。采用模块化方法来通过选择新平台，传感器或模型来轻松自定义模拟。我们通过对由此产生的政策进行基准测试，并通过一套完整的指标来证明我们的小说健身房的潜力，该政策接受了不同的导航任务。
Learning agents can optimize standard autonomous navigation improving flexibility, efficiency, and computational cost of the system by adopting a wide variety of approaches. This work introduces the \textit{PIC4rl-gym}, a fundamental modular framework to enhance navigation and learning research by mixing ROS2 and Gazebo, the standard tools of the robotics community, with Deep Reinforcement Learning (DRL). The paper describes the whole structure of the PIC4rl-gym, which fully integrates DRL agent's training and testing in several indoor and outdoor navigation scenarios and tasks. A modular approach is adopted to easily customize the simulation by selecting new platforms, sensors, or models. We demonstrate the potential of our novel gym by benchmarking the resulting policies, trained for different navigation tasks, with a complete set of metrics.
