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Deep TAMER: Interactive agent shaping in high-dimensional state spaces.
Garrett
Warnell, Nicholas Waytowich, Vernon Lawhern, and Peter Stone.
In Proceedings
of the Thirty-Second AAAI Conference on Artificial Intelligence, February 2018.
[PDF]1.6MB [slides.pptx]16.2MB
While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data. One way to increase the speed at which agents are able to learn to perform tasks is by leveraging the input of human trainers. Although such input can take many forms, real-time, scalar-valued feedback is especially useful in situations where it proves difficult or impossible for humans to provide expert demonstrations. Previous approaches have shown the usefulness of human input provided in this fashion (e.g., the TAMER framework), but they have thus far not considered high-dimensional state spaces or employed the use of deep learning. In this paper, we do both: we propose Deep TAMER, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer. We demonstrate Deep TAMER’s success by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of BOWLING - a task that has proven difficult for even state-of-the-art reinforcement learning methods.
@InProceedings{AAAI18-Warnell,
author = {Garrett Warnell and Nicholas Waytowich and Vernon Lawhern and Peter Stone},
title = {{Deep TAMER: Interactive agent shaping in high-dimensional state spaces}},
booktitle = {Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence},
location = {New Orleans, Louisiana, USA},
month = {February},
year = {2018},
abstract={
While recent advances in deep reinforcement learning have allowed
autonomous learning agents to succeed at a variety of complex tasks,
existing algorithms generally require a lot of training data. One way to
increase the speed at which agents are able to learn to perform tasks is
by leveraging the input of human trainers. Although such input can take
many forms, real-time, scalar-valued feedback is especially useful in
situations where it proves difficult or impossible for humans to provide
expert demonstrations. Previous approaches have shown the usefulness of
human input provided in this fashion (e.g., the TAMER framework), but they
have thus far not considered high-dimensional state spaces or employed
the use of deep learning. In this paper, we do both: we propose Deep
TAMER, an extension of the TAMER framework that leverages the
representational power of deep neural networks in order to learn complex
tasks in just a short amount of time with a human trainer. We demonstrate
Deep TAMER’s success by using it and just 15 minutes of human-provided
feedback to train an agent that performs better than humans on the Atari
game of BOWLING - a task that has proven difficult for even
state-of-the-art reinforcement learning methods.
}
}
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