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Imitation Learning: A Survey of Learning Methods

机译：模仿学习：学习方法概述

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摘要

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.
机译：模仿学习技术旨在模仿给定任务中的人类行为。通过学习观察值和动作之间的映射关系，对代理（学习机）进行培训，使其能够从演示中执行任务。模仿教学的思想已经存在了很多年。但是，由于计算和传感技术的进步以及对智能应用程序的需求不断增加，因此该领域最近受到关注。模仿学习的范式越来越受欢迎，因为它有助于以最少的专家知识来教授复杂的任务。通用的模仿学习方法可以将讲授任务的问题减少为提供演示的问题，而无需进行明确的编程或设计针对任务的奖励功能。现代传感器能够快速收集和传输大量数据，而具有高计算能力的处理器可以进行快速处理，从而将传感数据及时地映射到动作。这为许多需要实时感知和反应的潜在AI应用打开了大门，例如人形机器人，自动驾驶汽车，人机交互和计算机游戏等。然而，由于模仿学习带来了自己的挑战，因此需要专门的算法来有效而稳健地学习模型。在本文中，我们将调查模仿学习方法，并在学习过程的不同步骤中介绍设计选项。我们介绍了该领域的背景和动机，并重点介绍了模仿问题所面临的挑战。对设计和评估模仿学习任务的方法进行了分类和审查。特别要注意机器人技术和游戏中的学习方法，因为这些领域在文献中最受欢迎，并提供了各种各样的问题和方法。我们广泛讨论了使用不同的来源和方法结合模仿学习方法，以及结合其他运动学习方法来增强模仿的方法。我们还将讨论对行业的潜在影响，当前的主要应用，并重点介绍当前和未来的研究方向。

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