论文标题
分散的深钢筋学习，用于分布式和自适应的运动控制器
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot
论文作者
论文摘要
运动是动物自适应行为的一个典型例子，生物控制原理启发了腿部机器人的控制架构。尽管近年来，机器学习已成功地应用于许多任务，但在连续控制任务中应用于现实世界机器人时，深厚的强化学习方法似乎仍然在挣扎，尤其是不像可以很好地处理不确定性的强大解决方案。因此，将生物学原理纳入此类学习架构有新的兴趣。在诱导运动控制中发现的层次结构组织已经显示出一些成功的同时，我们在这里提出了一个在昆虫运动控制中发现的分散组织，以协调不同的腿。在模拟的Hexapod机器人上引入了分散和分布式体系结构，并通过深入的强化学习来学习控制器的细节。我们首先表明这种同时的本地结构能够学习更好的步行行为。其次，与整体方法相比，更简单的组织的学习速度更快。
Locomotion is a prime example for adaptive behavior in animals and biological control principles have inspired control architectures for legged robots. While machine learning has been successfully applied to many tasks in recent years, Deep Reinforcement Learning approaches still appear to struggle when applied to real world robots in continuous control tasks and in particular do not appear as robust solutions that can handle uncertainties well. Therefore, there is a new interest in incorporating biological principles into such learning architectures. While inducing a hierarchical organization as found in motor control has shown already some success, we here propose a decentralized organization as found in insect motor control for coordination of different legs. A decentralized and distributed architecture is introduced on a simulated hexapod robot and the details of the controller are learned through Deep Reinforcement Learning. We first show that such a concurrent local structure is able to learn better walking behavior. Secondly, that the simpler organization is learned faster compared to holistic approaches.
