Accession Number:

AD1011857

Title:

Curriculum Development for Transfer Learning in Dynamic Multiagent Settings

Descriptive Note:

Technical Report,01 Feb 2014,29 Feb 2016

Corporate Author:

University of Texas at Austin Austin United States

Report Date:

2016-06-01

Pagination or Media Count:

55.0

Abstract:

Transfer learning in reinforcement learning has been an active area of research over the past decade. In transfer learning, training on a source task is leveraged to speed up or otherwise improve learning on a target task. This project addressed the ambitious problem of curriculum learning in reinforcement learning, in which the goal is to design a sequence of source tasks for an agent to train on, such that final performance or learning speed is improved. We take the position that each stage of such a curriculum should be tailored to the ability of the agent in order to promote learning new behaviors. To tackle the problem of curriculum learning, we addressed three key sub-problems 1 Learning Transferability, and 2 Automatic Source Task Creation, 3 Curriculum Construction through Crowd Sourcing. This technical report documents the methods, experiments, and results of the proposed frameworks for curriculum construction for reinforcement learning agents.

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Distribution Statement:

APPROVED FOR PUBLIC RELEASE