论文标题
许多情节通过端到端的互动中的模块化体现代理学习
Many Episode Learning in a Modular Embodied Agent via End-to-End Interaction
论文作者
论文摘要
在这项工作中，我们对一个体现的机器学习（ML）动力代理进行了案例研究，该毒剂通过与群众的互动来改善自己。该代理由一组模块组成，其中一些模块是学到的，而另一些则是启发式的。尽管在ML意义上，代理不是“端到端”，但端到端的互动是代理学习机制的重要组成部分。我们描述了代理的设计如何与多个注释接口的设计一起工作，以允许人群工人从端到端交互中将信用分配给模块错误，并标记单个模块的数据。在多个自动化的人类代理人的互动，信用分配，数据注释以及模型重新训练和重新部署中，我们证明了代理的改善。
In this work we give a case study of an embodied machine-learning (ML) powered agent that improves itself via interactions with crowd-workers. The agent consists of a set of modules, some of which are learned, and others heuristic. While the agent is not "end-to-end" in the ML sense, end-to-end interaction is a vital part of the agent's learning mechanism. We describe how the design of the agent works together with the design of multiple annotation interfaces to allow crowd-workers to assign credit to module errors from end-to-end interactions, and to label data for individual modules. Over multiple automated human-agent interaction, credit assignment, data annotation, and model re-training and re-deployment, rounds we demonstrate agent improvement.
