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arXiv · 2204.08687

Many Episode Learning in a Modular Embodied Agent via End-to-End Interaction

Yuxuan Sun·Ethan Carlson·Rebecca Qian·Kavya Srinet·Arthur Szlam
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Abstract

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.

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Yuxuan Sun, Ethan Carlson, Rebecca Qian, Kavya Srinet, Arthur Szlam. 2022-04-19. Many Episode Learning in a Modular Embodied Agent via End-to-End Interaction. https://arxiv.org/abs/2204.08687

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