Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation | Paper Citation Record

Observation 5613138e-a2b7-4f09-b075-fa23f26b74ca · outbound

This paper cites Deep rein- forcement learning for dynamic treatment regimes on medical registry data.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Deep rein- forcement learning for dynamic treatment regimes on medical registry data

Reference 1

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Observation 3ff031b4-4c2d-4c0a-9c54-4fa6922eb086 · outbound

This paper cites Deep reinforcement learning for autonomous driving: A survey.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Deep reinforcement learning for autonomous driving: A survey

Reference 2

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Observation 4baa2b41-6a6a-4dce-8b3a-f3a1fef53fbd · outbound

This paper cites Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

Reference 3

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Observation fa5f8d35-7bd6-4766-924c-660ddfb74b70 · outbound

This paper cites Sim-to-real interactive recommendation via off-dynamics reinforcement learning.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Sim-to-real interactive recommendation via off-dynamics reinforcement learning

Reference 4

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Observation dd32523b-2d57-48a7-a4f0-b2c20d772568 · outbound

This paper cites DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning

Reference 5

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This paper cites Unsupervised domain adaptation with dynamics-aware rewards in reinforcement learning.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Unsupervised domain adaptation with dynamics-aware rewards in reinforcement learning

Reference 6

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Observation 7499ddd5-1a1b-4f04-9a6f-ce7f3de53280 · outbound

This paper cites Generative adversarial imitation learning.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Generative adversarial imitation learning

Reference 7

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Observation fd30c84c-8a34-4fd0-8d82-19e9a3177f5a · outbound

This paper cites Generative Adversarial Imitation from Observation.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Generative Adversarial Imitation from Observation

Reference 8

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Observation 67525ab4-c36a-43f7-ba45-e7263eda8d37 · outbound

This paper cites Offline imitation learning with a misspecified simulator.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Offline imitation learning with a misspecified simulator

Reference 9

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Observation 5fb528e4-6cfa-4290-a0b0-cef63400ccd0 · outbound

This paper cites An imitation from observation approach to transfer learning with dynamics mismatch.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation An imitation from observation approach to transfer learning with dynamics mismatch

Reference 10

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Observation 2673544c-0942-4e83-b05d-3a617f6e9d54 · outbound

This paper cites State-only Imitation with Transition Dynamics Mismatch.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation State-only Imitation with Transition Dynamics Mismatch

Reference 11

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This paper cites Doubly Robust Policy Evaluation and Learning.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Doubly Robust Policy Evaluation and Learning

Reference 12

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This paper cites Doubly robust off-policy value evaluation for reinforcement learning.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Doubly robust off-policy value evaluation for reinforcement learning

Reference 13

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Observation 3fa4eb3f-6143-4c6c-b60b-5c3ca1ec149c · outbound

This paper cites Doubly robust off-policy evaluation with shrinkage.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Doubly robust off-policy evaluation with shrinkage

Reference 14

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This paper cites Doubly robust off-policy actor-critic: Convergence and optimality.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Doubly robust off-policy actor-critic: Convergence and optimality

Reference 15

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Observation a67cd4fa-e58b-40a3-a2ee-0b12438c2009 · outbound

This paper cites Doubly robust distribu- tionally robust off-policy evaluation and learning.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Doubly robust distribu- tionally robust off-policy evaluation and learning

Reference 16

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Observation 838e9822-b528-4ded-88ef-3632000b9f7e · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 17

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Observation b521f84e-07f0-4a70-bd00-a9afb35559db · outbound

This paper cites On the off-dynamics approach to reinforcement learning.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation On the off-dynamics approach to reinforcement learning

Reference 18

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Observation 983a1df7-5f2d-4b03-b13b-48f96b33386d · outbound

This paper cites When to trust your model: Model-based policy optimization.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation When to trust your model: Model-based policy optimization

Reference 19

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Observation 2322f4d4-362a-4411-b663-48c7abd51685 · outbound

This paper cites Mutual alignment transfer learning.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Mutual alignment transfer learning

Reference 20

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Observation 859041a9-8267-4a5a-b3cc-e5099afa6451 · outbound

This paper cites Domain Adaptation for Reinforcement Learning on the Atari.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Domain Adaptation for Reinforcement Learning on the Atari

Reference 21

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This paper cites Domain adaptation in reinforcement learning via latent unified state representation.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Domain adaptation in reinforcement learning via latent unified state representation

Reference 22

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Observation 8df7979f-e2dd-4bd4-bc39-b81e5579f731 · outbound

This paper cites Transfer learning in deep reinforce- ment learning: A survey.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Transfer learning in deep reinforce- ment learning: A survey

Reference 23

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Observation 40b228c3-039a-44d7-b42e-dd51a0522d74 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 24

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Observation 1a0e4323-7e16-45d5-a78e-8ab3d648c68b · outbound

This paper cites State regularized policy optimization on data with dynamics shift.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation State regularized policy optimization on data with dynamics shift

Reference 25

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This paper cites Generative adversarial nets.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Generative adversarial nets

Reference 26

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This paper cites Distributionally robust off-dynamics reinforcement learning: Prov- able efficiency with linear function approximation.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Distributionally robust off-dynamics reinforcement learning: Prov- able efficiency with linear function approximation

Reference 27

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Observation 41eaee32-c7f3-45e9-b0c1-aaa0782819e2 · outbound

This paper cites Learning Robust Rewards with Adversarial Inverse Reinforcement Learning.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

Reference 28

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Observation a118e89a-86dd-401d-bf96-580d44b86363 · outbound

This paper cites Imitation learning via kernel mean embedding.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Imitation learning via kernel mean embedding

Reference 29

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Observation 8eb1bf54-3c9e-42ae-a0e2-dbb200a85835 · outbound

This paper cites Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

Reference 30

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Observation 6f4a63f3-1e08-4d69-9b0b-6e5b06b5f65b · outbound

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Task transfer by preference-based cost learning

Reference 31

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This paper cites Imitation Learning from Video by Leveraging Proprioception.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Imitation Learning from Video by Leveraging Proprioception

Reference 32

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Observation 16e2bad1-a861-4f73-a727-725b0cec41eb · outbound

This paper cites Imitation from observation: Learning to imitate behaviors from raw video via context translation.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Imitation from observation: Learning to imitate behaviors from raw video via context translation

Reference 33

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Observation e3e99ab7-cf1f-401e-ade8-3e44214fb1a7 · outbound

This paper cites Behavioral Cloning from Observation.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Behavioral Cloning from Observation

Reference 34

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This paper cites Recent Advances in Imitation Learning from Observation.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Recent Advances in Imitation Learning from Observation

Reference 35

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Domain adaptive imitation learning

Reference 36

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Generalization and equilibrium in generative adversarial nets (gans)

Reference 37

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation vf+MOWCXlXkD7CB/Zj9tqm3hyT0=

Reference 38

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation And in the introduc- tion section, we have a contribution list

Reference 39

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This paper cites Limitations.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Limitations

Reference 40

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This paper cites We present our theoretical result in Section 4 and the proof is in Appendix B.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation We present our theoretical result in Section 4 and the proof is in Appendix B

Reference 41

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This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Guidelines: • The answer NA means that the paper does not include experiments

Reference 42

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 43

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This paper cites We also describe the hyperparameter tuning in the Appendix D.4.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation We also describe the hyperparameter tuning in the Appendix D.4

Reference 44

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Guidelines: • The answer NA means that the paper does not include experiments

Reference 45

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Guidelines: • The answer NA means that the paper does not include experiments

Reference 46

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 47

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:17:36.836178Z

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Guidelines: • The answer NA means that the paper poses no such risks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:17:36.822673Z

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Guidelines: • The answer NA means that the paper does not use existing assets

Reference 50

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Observation aadab1a8-e210-4843-bc9d-b1dcb1ae1500 · outbound

This paper cites Also, details about the implementation are included in the paper.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Also, details about the implementation are included in the paper

Reference 51

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Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 52

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This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:17:36.773313Z

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