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Generalizing Curricula for Reinforcement Learning.
Sanmit Narvekar
and Peter Stone.
In 4th Lifelong Learning Workshop at the International
Conference on Machine Learning (ICML 2020), July 2020.
[PDF]330.4kB [slides.pdf]3.8MB
Curriculum learning for reinforcement learning (RL) is an active area of research that seeks to speed up training of RL agents on a target task by first training them through a series of progressively more challenging source tasks. Each task in this sequence builds upon skills learned in previous tasks to gradually develop the repertoire needed to solve the final task. Over the past few years, many automated methods to develop curricula have been developed. However, they all have one key limitation: the curriculum must be regenerated from scratch for each new agent or task encountered. In many cases, this generation process can be very expensive. However, there is structure that can be exploited between tasks and agents, such that knowledge gained developing a curriculum for one task can be reused to speed up creating a curriculum for a new task. In this paper, we present a method to generalize a curriculum learned for one set of tasks to a novel set of unseen tasks.
@InProceedings{ICML20-sanmit,
author = {Sanmit Narvekar and Peter Stone},
title = {Generalizing Curricula for Reinforcement Learning},
booktitle = {4th Lifelong Learning Workshop at the International Conference on Machine Learning (ICML 2020)},
location = {Vienna, Austria},
month = {July},
year = {2020},
abstract = {
Curriculum learning for reinforcement learning (RL) is an active area of
research that seeks to speed up training of RL agents on a target task
by first training them through a series of progressively more
challenging source tasks. Each task in this sequence builds upon skills
learned in previous tasks to gradually develop the repertoire needed to
solve the final task. Over the past few years, many automated methods
to develop curricula have been developed. However, they all have one
key limitation: the curriculum must be regenerated from scratch for
each new agent or task encountered. In many cases, this generation
process can be very expensive. However, there is structure that can be
exploited between tasks and agents, such that knowledge gained
developing a curriculum for one task can be reused to speed up creating
a curriculum for a new task. In this paper, we present a method to
generalize a curriculum learned for one set of tasks to a novel set of
unseen tasks.
},
}
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