A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making. Importantly, while it is the artificial agent that learns and acts, it is still up to humans to specify the particular task to be performed. Classical task-specification approaches typically involve humans providing stationary reward functions or explicit demonstrations of the desired tasks. However, there has recently been a great deal of research energy invested in exploring alternative ways in which humans may guide learning agents that may, e.g., be more suitable for certain tasks or require less human effort. This survey provides a high-level overview of five recent machine learning frameworks that primarily rely on human guidance apart from pre-specified reward functions or conventional, step-by-step action demonstrations. We review the motivation, assumptions, and implementation of each framework, and we discuss possible future research directions.

人工智能的一个长期目标是创建能够学习执行需要顺序决策的任务的人工智能。重要的是，虽然人工智能是学习和行动的，但仍然由人类来指定要执行的特定任务。经典的任务规范方法通常涉及人类提供固定奖励功能或所需任务的明确演示。然而，最近投入了大量的研究精力来探索人类可以指导学习代理的替代方式，这些方式可能例如更适合某些任务或需要较少的人力。这项调查提供了对五个最近的机器学习框架的高级概述，除了预先指定的奖励功能或传统的，主要依赖于人类指导的机器学习框架。一步一步的动作示范。我们回顾了每个框架的动机、假设和实施，并讨论了未来可能的研究方向。