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Importance Sampling Policy Evaluation with an Estimated Behavior Policy
In reinforcement learning, off-policy evaluation is the task of using da...
06/04/2018 ∙ by Josiah Hanna, et al. ∙
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Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
We establish a connection between the importance sampling estimators typ...
10/15/2019 ∙ by Yao Liu, et al. ∙
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Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
In importance sampling (IS)-based reinforcement learning algorithms such...
05/07/2019 ∙ by Seungyul Han, et al. ∙
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Importance Resampling for Off-policy Prediction
Importance sampling (IS) is a common reweighting strategy for off-policy...
06/11/2019 ∙ by Matthew Schlegel, et al. ∙
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The Importance of Pessimism in Fixed-Dataset Policy Optimization
We study worst-case guarantees on the expected return of fixed-dataset p...
09/15/2020 ∙ by Jacob Buckman, et al. ∙
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Asymptotic optimality of adaptive importance sampling
Adaptive importance sampling (AIS) uses past samples to update the sampl...
06/04/2018 ∙ by Bernard Delyon, et al. ∙
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Efficiency of adaptive importance sampling
The sampling policy of stage t, formally expressed as a probability dens...
06/04/2018 ∙ by Bernard Delyon, et al. ∙
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Conditional Importance Sampling for Off-Policy Learning
10/16/2019 ∙ by Mark Rowland, et al. ∙
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The principal contribution of this paper is a conceptual framework for off-policy reinforcement learning, based on conditional expectations of importance sampling ratios. This framework yields new perspectives and understanding of existing off-policy algorithms, and reveals a broad space of unexplored algorithms. We theoretically analyse this space, and concretely investigate several algorithms that arise from this framework.
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