IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning | Paper Citation Record

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Unresolved cited work

Reference 1

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Integrated architectures for learning, planning, and reacting based on approximating dynamic programming,

Reference 2

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This paper cites A survey on model-based reinforcement learning,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning A survey on model-based reinforcement learning,

Reference 3

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Pilco: A model-based and data-efficient approach to policy search,

Reference 4

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Guided policy search,

Reference 5

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This paper cites Dream to control: Learning behaviors by latent imagination,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Dream to control: Learning behaviors by latent imagination,

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Model- based reinforcement learning: A survey,

Reference 7

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This paper cites Mastering atari, go, chess and shogi by planning with a learned model,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Mastering atari, go, chess and shogi by planning with a learned model,

Reference 8

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

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning When to trust your model: Model-based policy optimization,

Reference 9

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Dyna, an integrated architecture for learning, planning, and reacting,

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Model- ensemble trust-region policy optimization,

Reference 12

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This paper cites Improving multi-step prediction of learned time series models,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Improving multi-step prediction of learned time series models,

Reference 13

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This paper cites Lipschitz continuity in model- based reinforcement learning,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Lipschitz continuity in model- based reinforcement learning,

Reference 14

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This paper cites Adversarial counterfactual environment model learning,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Adversarial counterfactual environment model learning,

Reference 15

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This paper cites Generative adversarial imitation learning,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Generative adversarial imitation learning,

Reference 16

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This paper cites Off-policy deep reinforcement learning without exploration,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Off-policy deep reinforcement learning without exploration,

Reference 17

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Conservative q-learning for offline reinforcement learning,

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This paper cites Trust the model where it trusts itself - model-based actor-critic with uncertainty-aware rollout adaption,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Trust the model where it trusts itself - model-based actor-critic with uncertainty-aware rollout adaption,

Reference 19

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Unresolved cited work

Reference 20

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Unresolved cited work

Reference 21

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This paper cites Continuous deep q- learning with model-based acceleration,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Continuous deep q- learning with model-based acceleration,

Reference 22

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This paper cites Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning,.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning,

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Improving pilco with bayesian neural network dynamics models,

Reference 24

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Sample- efficient reinforcement learning with stochastic ensemble value expan- sion,

Reference 25

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Deep reinforce- ment learning in a handful of trials using probabilistic dynamics models,

Reference 26

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

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 27

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Uncertainty-based offline reinforcement learning with diversified q-ensemble,

Reference 28

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Adversarially trained actor critic for offline reinforcement learning,

Reference 29

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Mopo: Model-based offline policy optimization,

Reference 30

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Morel: Model-based offline reinforcement learning,

Reference 31

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning A review of off-policy evaluation in reinforcement learning,

Reference 32

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Marginal mean models for dynamic regimes,

Reference 33

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Doubly robust policy evaluation and learning,

Reference 34

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated bi- nary outcome,

Reference 35

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Eligibility traces for off-policy policy evaluation,

Reference 36

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning The central role of the propensity score in observational studies for causal effects,

Reference 37

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning More robust doubly robust off-policy evaluation,

Reference 38

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Estimation of regression coefficients when some regressors are not always observed,

Reference 39

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Doubly robust off-policy value evaluation for rein- forcement learning,

Reference 40

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Data-efficient off-policy policy evaluation for reinforcement learning,

Reference 41

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning More efficient off-policy evaluation through regularized targeted learning,

Reference 42

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Targeted maximum likelihood learning,

Reference 43

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Policy gradi- ent methods for reinforcement learning with function approximation,

Reference 44

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Nonlinear ica using auxiliary variables and generalized contrastive learning,

Reference 45

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Mujoco: A physics engine for model- based control,

Reference 46

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 47

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IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees,

Reference 48

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This paper cites VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments.

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments

Reference 49

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