论文标题
通过深入的强化学习获得多机器人导航的强大控制和导航政策
Obtaining Robust Control and Navigation Policies for Multi-Robot Navigation via Deep Reinforcement Learning
论文作者
论文摘要
多机器人导航是一项具有挑战性的任务，其中必须在动态环境中同时协调多个机器人。我们应用深入的增强学习（DRL）来学习一个分散的端到端策略，该政策将原始传感器数据映射到代理的命令速度。为了使政策概括，培训是在不同的环境和场景中进行的。在常见的多机器人场景中测试和评估了学识渊博的政策，例如切换一个地方，交叉点和瓶颈情况。此策略允许代理从死端恢复并在复杂的环境中导航。
Multi-robot navigation is a challenging task in which multiple robots must be coordinated simultaneously within dynamic environments. We apply deep reinforcement learning (DRL) to learn a decentralized end-to-end policy which maps raw sensor data to the command velocities of the agent. In order to enable the policy to generalize, the training is performed in different environments and scenarios. The learned policy is tested and evaluated in common multi-robot scenarios like switching a place, an intersection and a bottleneck situation. This policy allows the agent to recover from dead ends and to navigate through complex environments.
