Chrome Extension
WeChat Mini Program
Use on ChatGLM

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

arXiv (Cornell University)(2023)

Cited 0|Views70
No score
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.
More
Translated text
Key words
modular embodied agent,many episode learning,interaction,end-to-end
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined