Curriculum Learning for Reinforcement Learning is an increasingly popular technique that involves training an agent on a sequence of intermediate tasks, called a Curriculum, to increase the agent's performance and learning speed. This paper introduces a novel paradigm for curriculum generation based on progression and mapping functions. While progression functions specify the complexity of the environment at any given time, mapping functions generate environments of a specific complexity. Different progression functions are introduced, including an autonomous online task progression based on the agent's performance. Our approach's benefits and wide applicability are shown by empirically comparing its performance to two state-of-the-art Curriculum Learning algorithms on six domains.
翻译:强化学习课程学习是一种日益流行的技术,它涉及对一名代理人员进行一系列中期任务的培训,称为课程,以提高该代理人员的绩效和学习速度。本文介绍了根据进度和绘图功能编制课程的新模式。虽然分级功能具体指明了任何特定时间环境的复杂性,但绘图功能产生具体复杂的环境。引入了不同的分级功能,包括根据该代理人员的业绩自主进行在线任务进展。我们的方法的好处和广泛适用性表现在将其业绩与六个领域的两种最先进的课程学习算法进行经验性比较。