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Learning Markov State Abstractions for Deep Reinforcement Learning
The fundamental assumption of reinforcement learning in Markov decision ...
06/08/2021 ∙ by Cameron Allen, et al. ∙
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Learning to search efficiently for causally near-optimal treatments
Finding an effective medical treatment often requires a search by trial ...
07/02/2020 ∙ by Samuel Håkansson, et al. ∙
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Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Off-policy evaluation in reinforcement learning offers the chance of usi...
02/10/2020 ∙ by Omer Gottesman, et al. ∙
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A general method for regularizing tensor decomposition methods via pseudo-data
Tensor decomposition methods allow us to learn the parameters of latent ...
05/24/2019 ∙ by Omer Gottesman, et al. ∙
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Combining Parametric and Nonparametric Models for Off-Policy Evaluation
We consider a model-based approach to perform batch off-policy evaluatio...
05/14/2019 ∙ by Omer Gottesman, et al. ∙
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Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning
Sepsis is the leading cause of mortality in the ICU. It is challenging t...
01/15/2019 ∙ by Xuefeng Peng, et al. ∙
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Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
In this work, we consider the problem of estimating a behaviour policy f...
07/03/2018 ∙ by Aniruddh Raghu, et al. ∙
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Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Much attention has been devoted recently to the development of machine l...
05/31/2018 ∙ by Omer Gottesman, et al. ∙
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Representation Balancing MDPs for Off-Policy Policy Evaluation
We study the problem of off-policy policy evaluation (OPPE) in RL. In co...
05/23/2018 ∙ by Yao Liu, et al. ∙
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Weighted Tensor Decomposition for Learning Latent Variables with Partial Data
Tensor decomposition methods are popular tools for learning latent varia...
10/18/2017 ∙ by Omer Gottesman, et al. ∙
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