Many Episode Learning in a Modular Embodied Agent via End-to-End
Interaction
- URL: http://arxiv.org/abs/2204.08687v1
- Date: Tue, 19 Apr 2022 06:11:46 GMT
- Title: Many Episode Learning in a Modular Embodied Agent via End-to-End
Interaction
- Authors: Yuxuan Sun, Ethan Carlson, Rebecca Qian, Kavya Srinet, Arthur Szlam
- Abstract summary: 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.
We describe how the design of the agent works together with the design of multiple annotation interfaces.
- Score: 22.14911101362573
- License: http://creativecommons.org/licenses/by/4.0/
- 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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