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Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey.
Sanmit
Narvekar, Bei Peng, Matteo Leonetti, Jivko
Sinapov, Matthew E. Taylor, and Peter
Stone.
Journal of Machine Learning Research, 21(181):1–50, 2020.
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged when starting to learn the next, harder task. More recently, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch. In this article, we present a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals. Finally, we use our framework to find open problems and suggest directions for future RL curriculum learning research.
@article{JMLR20-sanmit,
author = {Sanmit Narvekar and Bei Peng and Matteo Leonetti and Jivko Sinapov and Matthew E. Taylor and Peter Stone},
title = {Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey},
journal = {Journal of Machine Learning Research},
year = {2020},
volume = {21},
number = {181},
pages = {1-50},
url = {http://jmlr.org/papers/v21/20-212.html},
abstract = {
Reinforcement learning (RL) is a popular paradigm for addressing
sequential decision tasks in which the agent has only limited
environmental feedback. Despite many advances over the past three
decades, learning in many domains still requires a large amount of
interaction with the environment, which can be prohibitively
expensive in realistic scenarios. To address this problem, transfer
learning has been applied to reinforcement learning such that
experience gained in one task can be leveraged when starting to learn
the next, harder task. More recently, several lines of research have
explored how tasks, or data samples themselves, can be sequenced into
a curriculum for the purpose of learning a problem that may otherwise
be too difficult to learn from scratch. In this article, we present a
framework for curriculum learning (CL) in reinforcement learning, and
use it to survey and classify existing CL methods in terms of their
assumptions, capabilities, and goals. Finally, we use our framework
to find open problems and suggest directions for future RL curriculum
learning research.},
}
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