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DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation.
Faraz
Torabi, Garrett Warnell, and Peter
Stone.
In Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September
2021.
Video presentation
In imitation learning from observation (IfO), a learning agent seeks to imitatea demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. In this work, we hypothesize that we can incorporate ideas from model-based reinforcement learning with adversarial methods for IfO in order to increase the data efficiency of these methods without sacrificing performance. Specifically, we consider time-varying linear Gaussian policies, and propose a method that integrates the linear-quadratic regulator with path integral policy improvement into an existing adversarial IfO framework. The result is a more data-efficient IfO algorithm with better performance, which we show empirically in four simulation domains: using far fewer interactions with the environment, the proposed method exhibits similar or better performance than the existing technique.
@InProceedings{IROS2021-torabi,
author = {Faraz Torabi and Garrett Warnell and Peter Stone},
title = {DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation},
booktitle = {Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
location = {Prague, Czech Republic},
month = {September},
year = {2021},
abstract = {
In imitation learning from observation (IfO), a learning agent seeks to imitate
a demonstrating agent using only observations of the demonstrated behavior
without access to the control signals generated by the demonstrator. Recent
methods based on adversarial imitation learning have led to state-of-the-art
performance on IfO problems, but they typically suffer from high sample
complexity due to a reliance on data-inefficient, model-free reinforcement
learning algorithms. This issue makes them impractical to deploy in real-world
settings, where gathering samples can incur high costs in terms of time,
energy, and risk. In this work, we hypothesize that we can incorporate ideas
from model-based reinforcement learning with adversarial methods for IfO in
order to increase the data efficiency of these methods without sacrificing
performance. Specifically, we consider time-varying linear Gaussian policies,
and propose a method that integrates the linear-quadratic regulator with path
integral policy improvement into an existing adversarial IfO framework. The
result is a more data-efficient IfO algorithm with better performance, which
we show empirically in four simulation domains: using far fewer interactions
with the environment, the proposed method exhibits similar or better
performance than the existing technique.
},
wwwnote = {<a href="https://www.youtube.com/watch?v=o3t0mo_o7W8">Video presentation</a>}
}
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