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In this work we give a case study of an embodied machine-learning (ML)powered agent that improves itself via interactions with crowd-workers. Theagent consists of a set of modules, some of which are learned, and othersheuristic. While the agent is not "end-to-end" in the ML sense, end-to-endinteraction is a vital part of the agent's learning mechanism. We describe howthe design of the agent works together with the design of multiple annotationinterfaces to allow crowd-workers to assign credit to module errors fromend-to-end interactions, and to label data for individual modules. Overmultiple automated human-agent interaction, credit assignment, data annotation,and model re-training and re-deployment, rounds we demonstrate agentimprovement.

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