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We consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks.
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Xie, T · 2019
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Is a good representation sufficient for sample efficient reinforcement learning?
Du, S. S · 2020
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Duan, Y · 2020
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A theoretical analysis of deep q-learning
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Human-level control through deep reinforcement learning
Mnih, V · 2015
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Deep nets for local manifold learning
Chui, C. K · 2016
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Goodfellow, I. J · 2016
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Doubly robust off-policy value evaluation for reinforcement learning
Jiang, N · 2016
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Data-efficient off-policy policy evaluation for reinforcement learning
Thomas, P · 2016
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Learning cooperative visual dialog agents with deep reinforcement learning
Das, A · 2017
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Double reinforcement learning for efficient off-policy evaluation in markov decision processes
Kallus, N · 2020
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Minimax weight and q-function learning for off-policy evaluation
Uehara, M · 2020
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Q* approximation schemes for batch reinforcement learning: A theoretical comparison
Xie, T · 2020
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Asymptotically efficient off-policy evaluation for tabular reinforcement learning
Yin, M · 2020
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Nonparametric regression on low-dimensional manifolds using deep relu networks : Function approximation and statistical recovery
Chen, M · 2021
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Optimal policy evaluation using kernel-based temporal difference methods
Duan, Y · 2021
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Benchmarks for deep off-policy evaluation
Fu, J · 2021
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A deep reinforcement learning approach to marginalized importance sampling with the successor representation
Fujimoto, S · 2021
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Bootstrapping fitted q-evaluation for off-policy inference
Hao, B · 2021
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Besov function approximation and binary classification on low-dimensional manifolds using convolutional residual networks
Liu, H · 2021
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Variance-aware off-policy evaluation with linear function approximation
Min, Y · 2021
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Sample complexity of offline reinforcement learning with deep relu networks
Nguyen-Tang, T · 2021
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What are the statistical limits of offline RL with linear function approximation?
Wang, R · 2021
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Bellman-consistent pessimism for offline reinforcement learning
Xie, T · 2021
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Settling the sample complexity of model-based offline reinforcement learning
Li, G · 2022
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Liu, H · 2022
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Offline reinforcement learning with realizability and single-policy concentrability
Zhan, W · 2022
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Zhang, R · 2022
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