论文标题
机器人通过内在动机和自动课程学习学习越来越复杂的任务
Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning
论文作者
论文摘要
机器人的多任务学习构成了领域知识的挑战：任务的复杂性，所需的动作的复杂性，转移学习任务之间的关系。我们证明，可以学习这种领域知识来应对终身学习中的挑战。具体而言，各种复杂性的任务之间的层次结构是从简单到复合任务推断课程的关键。我们为机器人提出了一个框架，以学习无限复杂性的动作序列，以实现各种复杂性的多个控制任务。 我们的分层增强学习框架（名为SGIM-SAHT）提供了一个新的研究方向，并试图统一机器人武器和移动机器人的部分实现。我们概述了使机器人能够将多个控制任务映射到动作序列的贡献：任务依赖性的表示，本质上动机的探索以学习任务层次结构以及主动的模仿学习。在学习任务的层次结构时，它通过决定首先探索哪些任务，如何转移知识以及何时，如何模仿的任务来渗透其课程。
Multi-task learning by robots poses the challenge of the domain knowledge: complexity of tasks, complexity of the actions required, relationship between tasks for transfer learning. We demonstrate that this domain knowledge can be learned to address the challenges in life-long learning. Specifically, the hierarchy between tasks of various complexities is key to infer a curriculum from simple to composite tasks. We propose a framework for robots to learn sequences of actions of unbounded complexity in order to achieve multiple control tasks of various complexity. Our hierarchical reinforcement learning framework, named SGIM-SAHT, offers a new direction of research, and tries to unify partial implementations on robot arms and mobile robots. We outline our contributions to enable robots to map multiple control tasks to sequences of actions: representations of task dependencies, an intrinsically motivated exploration to learn task hierarchies, and active imitation learning. While learning the hierarchy of tasks, it infers its curriculum by deciding which tasks to explore first, how to transfer knowledge, and when, how and whom to imitate.
