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A synthesis of automated planning and reinforcement learning for efficient, robust decision-making.
Matteo
Leonetti, Luca Iocchi, and Peter Stone.
Artificial Intelligence,
241:103 – 130, September 2016.
Abstract Automated planning and reinforcement learning are characterized by complementary views on decision making: the former relies on previous knowledge and computation, while the latter on interaction with the world, and experience. Planning allows robots to carry out different tasks in the same domain, without the need to acquire knowledge about each one of them, but relies strongly on the accuracy of the model. Reinforcement learning, on the other hand, does not require previous knowledge, and allows robots to robustly adapt to the environment, but often necessitates an infeasible amount of experience. We present Domain Approximation for Reinforcement LearnING (DARLING), a method that takes advantage of planning to constrain the behavior of the agent to reasonable choices, and of reinforcement learning to adapt to the environment, and increase the reliability of the decision making process. We demonstrate the effectiveness of the proposed method on a service robot, carrying out a variety of tasks in an office building. We find that when the robot makes decisions by planning alone on a given model it often fails, and when it makes decisions by reinforcement learning alone it often cannot complete its tasks in a reasonable amount of time. When employing DARLING, even when seeded with the same model that was used for planning alone, however, the robot can quickly learn a behavior to carry out all the tasks, improves over time, and adapts to the environment as it changes.
@article{AIJ16-leonetti,
title = "A synthesis of automated planning and reinforcement learning for
efficient, robust decision-making",
journal = "Artificial Intelligence",
volume = "241",
pages = "103 - 130",
year = "2016",
month = "September",
issn = "0004-3702",
doi = "http://dx.doi.org/10.1016/j.artint.2016.07.004",
url = "http://www.sciencedirect.com/science/article/pii/S0004370216300819",
author = "Matteo Leonetti and Luca Iocchi and Peter Stone",
abstract = {Abstract Automated planning and reinforcement learning are
characterized by complementary views on decision making: the former relies on
previous knowledge and computation, while the latter on interaction with the
world, and experience. Planning allows robots to carry out different tasks in
the same domain, without the need to acquire knowledge about each one of them,
but relies strongly on the accuracy of the model. Reinforcement learning, on the
other hand, does not require previous knowledge, and allows robots to robustly
adapt to the environment, but often necessitates an infeasible amount of
experience. We present Domain Approximation for Reinforcement LearnING
(DARLING), a method that takes advantage of planning to constrain the behavior
of the agent to reasonable choices, and of reinforcement learning to adapt to
the environment, and increase the reliability of the decision making process. We
demonstrate the effectiveness of the proposed method on a service robot,
carrying out a variety of tasks in an office building. We find that when the
robot makes decisions by planning alone on a given model it often fails, and
when it makes decisions by reinforcement learning alone it often cannot complete
its tasks in a reasonable amount of time. When employing DARLING, even when
seeded with the same model that was used for planning alone, however, the robot
can quickly learn a behavior to carry out all the tasks, improves over time, and
adapts to the environment as it changes.},
}
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