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Importance Sampling Policy Evaluation with an Estimated Behavior Policy.
Josiah
Hanna, Scott Niekum, and Peter
Stone.
In Proceedings of the 36th International Conference on Machine Learning (ICML), June 2019.
We consider the problem of off-policy evaluation in Markov decision processes. Off-policy evaluation is the task of evaluating the expected return of one policy with data generated by a different, behavior policy. Importance sampling is a technique for off-policy evaluation that re-weights off-policy returns to account for differences in the likelihood of the returns between the two policies. In this paper, we study importance sampling with an estimated behavior policy where the behavior policy estimate comes from the same set of data used to compute the importance sampling estimate. We find that this estimator often lowers the mean squared error of off-policy evaluation compared to importance sampling with the true behavior policy or using a behavior policy that is estimated from a separate data set. Intuitively, estimating the behavior policy in this way corrects for error due to sampling in the action-space. Our empirical results also extend to other popular variants of importance sampling and show that estimating a non-Markovian behavior policy can further lower large-sample mean squared error even when the true behavior policy is Markovian.
@InProceedings{ICML2019-Hanna,
author={Josiah Hanna and Scott Niekum and Peter Stone},
title={Importance Sampling Policy Evaluation with an Estimated Behavior Policy},
booktitle={Proceedings of the 36th International Conference on Machine Learning (ICML)},
location={Long Beach, California, U.S.A.},
month={June},
year={2019},
abstract={
We consider the problem of off-policy evaluation in Markov decision processes.
Off-policy evaluation is the task of evaluating the expected return of one
policy with data generated by a different, behavior policy. Importance sampling
is a technique for off-policy evaluation that re-weights off-policy returns to
account for differences in the likelihood of the returns between the two
policies. In this paper, we study importance sampling with an estimated behavior
policy where the behavior policy estimate comes from the same set of data used
to compute the importance sampling estimate. We find that this estimator often
lowers the mean squared error of off-policy evaluation compared to importance
sampling with the true behavior policy or using a behavior policy that is
estimated from a separate data set. Intuitively, estimating the behavior policy
in this way corrects for error due to sampling in the action-space. Our
empirical results also extend to other popular variants of importance sampling
and show that estimating a non-Markovian behavior policy can further lower
large-sample mean squared error even when the true behavior policy is
Markovian.
},
}
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