论文标题
深入的强化学习面向现实世界动态场景
Deep reinforcement learning oriented for real world dynamic scenarios
论文作者
论文摘要
动态环境中的自主导航是自主机器人的复杂但必不可少的任务。最近的深入强化学习方法显示出有希望的结果可以解决该问题，但尚未解决，因为他们通常没有任何机器人运动动力学限制，自动运动或完美的环境知识。此外，由于无法为可能场景的巨大差异生成现实世界中的培训数据，因此大多数算法在现实世界中失败。在这项工作中，我们提出了一个新颖的计划者DQN-DOV，该计划使用了对描述性的Rob中心速度空间模型的深入增强学习，以在高度动态的环境中导航。它是在忠实地重现现实世界的模拟器上使用智能课程学习方法训练的，从而减少了现实与模拟之间的差距。我们在具有不同障碍物数量的方案中测试所得算法，并将其与许多最新方法进行比较，从而获得更好的性能。最后，我们使用与仿真实验相同的设置尝试了地面机器人中的算法。
Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots. Recent deep reinforcement learning approaches show promising results to solve the problem, but it is not solved yet, as they typically assume no robot kinodynamic restrictions, holonomic movement or perfect environment knowledge. Moreover, most algorithms fail in the real world due to the inability to generate real-world training data for the huge variability of possible scenarios. In this work, we present a novel planner, DQN-DOVS, that uses deep reinforcement learning on a descriptive robocentric velocity space model to navigate in highly dynamic environments. It is trained using a smart curriculum learning approach on a simulator that faithfully reproduces the real world, reducing the gap between the reality and simulation. We test the resulting algorithm in scenarios with different number of obstacles and compare it with many state-of-the-art approaches, obtaining a better performance. Finally, we try the algorithm in a ground robot, using the same setup as in the simulation experiments.
