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Task Factorization in Curriculum Learning.
Reuth Mirsky,
Shahaf S. Shperberg, Yulin Zhang, Zifan
Xu, Yuqian Jiang, Jiaxun Cui, and Peter
Stone.
In ICML workshop on Decision Awareness in Reinforcement Learning (DARL), July 2022.
recorded
presentation
A common challenge for learning when applied to a complex ``target'' task is that learning that task all at once can be too difficult due to inefficient exploration given a sparse reward signal. Curriculum Learning addresses this challenge by sequencing training tasks for a learner to facilitate gradual learning. One of the crucial steps in finding a suitable curriculum learning approach is to understand the dimensions along which the domain can be factorized. In this paper, we identify different types of factorizations common in the literature of curriculum learning for reinforcement learning tasks: factorizations that involve the agent, the environment, or the mission. For each factorization category, we identify the relevant algorithms and techniques that leverage that factorization and present several case studies to showcase how leveraging an appropriate factorization can boost learning using a simple curriculum.
@InProceedings{DARL22-REUTH,
author = {Reuth Mirsky and Shahaf S. Shperberg and Yulin Zhang and Zifan Xu and Yuqian Jiang and Jiaxun Cui and Peter Stone},
title = {Task Factorization in Curriculum Learning},
booktitle = {ICML workshop on Decision Awareness in Reinforcement Learning (DARL)},
location = {Baltimore, Maryland, USA},
month = {July},
year = {2022},
abstract = {
A common challenge for learning when applied to a complex ``target'' task is
that learning that task all at once can be too difficult due to inefficient
exploration given a sparse reward signal. Curriculum Learning addresses this
challenge by sequencing training tasks for a learner to facilitate gradual
learning. One of the crucial steps in finding a suitable curriculum learning
approach is to understand the dimensions along which the domain can be
factorized. In this paper, we identify different types of factorizations
common in the literature of curriculum learning for reinforcement learning
tasks: factorizations that involve the agent, the environment, or the
mission. For each factorization category, we identify the relevant algorithms
and techniques that leverage that factorization and present several case
studies to showcase how leveraging an appropriate factorization can boost
learning using a simple curriculum.
},
wwwnote={<a href="https://slideslive.com/38987380">recorded presentation</a>},
}
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