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Gaussian processes for sample efficient reinforcement learning with RMAX-like exploration.
Tobias
Jung and Peter Stone.
In The European Conference on Machine Learning
(ECML), September 2010.
[PDF]417.0kB [postscript]6.5MB [slides.pdf]505.6kB
We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achieve low sample complexity. To achieve low sample complexity, since the environment is unknown, an agent must intelligently balance exploration and exploitation, and must be able to rapidly generalize from observations. While in the past a number of related sample efficient RL algorithms have been proposed, to allow theoretical analysis, mainly model-learners with weak generalization capabilities were considered. Here, we separate function approximation in the model-learner (which does require samples) from the interpolation in the planner (which does not require samples). For model-learning we apply Gaussian processes regression (GP) which is able to automatically adjust itself to the complexity of the problem (via Bayesian hyperparameter selection) and, in practice, often able to learn a highly accurate model from very little data. In addition, a GP provides a natural way to determine the uncertainty of its predictions, which allows us to implement the ``optimism in the face of uncertainty'' principle used to efficiently control exploration. Our method is evaluated on four common benchmark domains.
@InProceedings{ECML10-jung,
author = "Tobias Jung and Peter Stone",
title = "Gaussian processes for sample efficient reinforcement learning with {RMAX}-like exploration",
booktitle = "The European Conference on Machine Learning (ECML)",
month = "September",
year = "2010",
abstract = {
We present an implementation of model-based online
reinforcement learning (RL) for continuous domains with deterministic
transitions that is specifically designed to achieve low sample
complexity. To achieve low sample complexity, since the environment is
unknown, an agent must intelligently balance exploration and
exploitation, and must be able to rapidly generalize from observations.
While in the past a number of related sample efficient RL algorithms
have been proposed, to allow theoretical analysis, mainly model-learners
with weak generalization capabilities were considered. Here, we separate
function approximation in the model-learner (which does require samples)
from the interpolation in the planner (which does not require samples).
For model-learning we apply Gaussian processes regression (GP) which is
able to automatically adjust itself to the complexity of the problem
(via Bayesian hyperparameter selection) and, in practice, often able to
learn a highly accurate model from very little data. In addition, a GP
provides a natural way to determine the uncertainty of its predictions,
which allows us to implement the ``optimism in the face of uncertainty''
principle used to efficiently control exploration. Our method is
evaluated on four common benchmark domains.
},
}
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