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

Observation 28b27ca2-c7ee-4184-83a1-6292f33be2ce · outbound

This paper cites an unresolved cited work.

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

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-12T20:36:21.284401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f02e2f1a-cf4c-44f8-b1a4-addaef8ec3be · outbound

This paper cites Integrated architectures for learning, planning, and reacting based on approximating dynamic programming,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:21.265235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 45a925a3-33fc-4e69-9eb4-b160bd8f136a · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.191188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58e7f571-3543-461a-9baf-56f751164edc · outbound

This paper cites Pilco: A model-based and data-efficient approach to policy search,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:21.232670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f34b775-ac32-4beb-ae72-ca2256f3d397 · outbound

This paper cites Guided policy search,.

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

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:21.213586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 995b08b3-c80c-41cf-af88-210166c92694 · outbound

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,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.210676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e75ac4ca-c976-4cdf-9b96-beb1d4f35868 · outbound

This paper cites Model- based reinforcement learning: A survey,.

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

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:21.183384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c896dfa0-ba87-44d1-b083-777145438253 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.223410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cb31a0f-9544-4027-83ae-23b71454b247 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.229013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 504de25b-14ba-4c70-8b52-e3f9caa80379 · outbound

This paper cites Dyna, an integrated architecture for learning, planning, and reacting,.

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

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.234867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61dce01c-3da3-498c-a710-d932218e9254 · outbound

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

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,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.240120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1ada517-e2f2-4c66-86a7-cbdf16c094a1 · outbound

This paper cites Model- ensemble trust-region policy optimization,.

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

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:21.115480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a9f9fddf-8cce-4702-b00e-a2e106782360 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:21.098144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 73b0b102-65bc-4404-8cc3-7ad5440de09b · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:21.081203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dc98699a-6ec9-4557-9e59-8a6ba853c707 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:21.063766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 22692b15-6104-4750-a73e-c7be37d9e644 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.267264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97c20a73-58a5-4de2-b387-a296aca14ff0 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.272388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e693515d-4dda-4898-bf30-8221e08ae0ac · outbound

This paper cites Conservative q-learning for offline reinforcement learning,.

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

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.277428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 314fda7a-e4ba-4643-aac9-b0173e31dfa1 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:21.010586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 14afb53a-9d66-40e3-a29a-eb81221c133a · outbound

This paper cites an unresolved cited work.

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

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-12T20:36:20.992278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c790d0c-c3f7-4ad6-afb5-d207ba74c0f7 · outbound

This paper cites an unresolved cited work.

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

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-12T20:36:20.973562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e95ccb00-28bd-4029-9523-8a1b3dd2eb89 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.954421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c59a48b8-f357-49e2-99f9-07049c16dea1 · outbound

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,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.308186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bb7dbe4-2ed9-4c24-8d2f-f9d310e9897f · outbound

This paper cites Improving pilco with bayesian neural network dynamics models,.

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

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.923589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7eb79ea2-afe9-4084-b0e6-db74fac64df7 · outbound

This paper cites Sample- efficient reinforcement learning with stochastic ensemble value expan- sion,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.905022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 94d0768a-f953-4617-aa0e-781c14e8bf8c · outbound

This paper cites Deep reinforce- ment learning in a handful of trials using probabilistic dynamics models,.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.325577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fccfbbf-34c9-44cf-94fe-1276027f7efa · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.331828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecf56e25-1b4e-4f77-bd5a-d4481f4a5756 · outbound

This paper cites Uncertainty-based offline reinforcement learning with diversified q-ensemble,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.875012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f3ad808-4563-454e-ac38-4d5f8161f3c7 · outbound

This paper cites Adversarially trained actor critic for offline reinforcement learning,.

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

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.855922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1d78be10-6b49-4a70-b76b-eddf036d538c · outbound

This paper cites Mopo: Model-based offline policy optimization,.

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

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.347942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27a348e7-2fe2-4a81-b5dc-366b7c4c7b68 · outbound

This paper cites Morel: Model-based offline reinforcement learning,.

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

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.353342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e7970f8-90ce-4342-a844-1e31d6093bab · outbound

This paper cites A review of off-policy evaluation in reinforcement learning,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.814039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5133181f-e575-49db-866f-1bf5c76af332 · outbound

This paper cites Marginal mean models for dynamic regimes,.

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

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.796441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 36ecd89f-e326-4b22-8755-ff5bae80124c · outbound

This paper cites Doubly robust policy evaluation and learning,.

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

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.778990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9a8dd063-e27a-41ba-b611-d4efe158a887 · outbound

This paper cites Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated bi- nary outcome,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.761862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 22d8fadb-4df1-4764-bec9-ca285aca10bb · outbound

This paper cites Eligibility traces for off-policy policy evaluation,.

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

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.744467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 89e7a820-645e-4e3a-ae7e-97ac8674db67 · outbound

This paper cites The central role of the propensity score in observational studies for causal effects,.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.386672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87d1898e-f1be-4eac-94b9-215836f69379 · outbound

This paper cites More robust doubly robust off-policy evaluation,.

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

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.713942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1f4f5dbf-f02b-49ab-845a-0d4c9e93738a · outbound

This paper cites Estimation of regression coefficients when some regressors are not always observed,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.696146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ec4179e0-81b1-4508-af59-1e4256f2068d · outbound

This paper cites Doubly robust off-policy value evaluation for rein- forcement learning,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.677203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation adfa28ff-b8ec-4853-b12e-1f6b7155db90 · outbound

This paper cites Data-efficient off-policy policy evaluation for reinforcement learning,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.657617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2edb2bd2-2073-4406-9598-1b8861cbb514 · outbound

This paper cites More efficient off-policy evaluation through regularized targeted learning,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.638625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 283a0426-3aa0-47bd-8719-76c3367c1a39 · outbound

This paper cites Targeted maximum likelihood learning,.

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

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.619129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 31e55c0a-d63c-4407-9ee6-4597563f95c0 · outbound

This paper cites Policy gradi- ent methods for reinforcement learning with function approximation,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.598157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1e8fa356-5a1b-4b89-81a7-cd9078f35a96 · outbound

This paper cites Nonlinear ica using auxiliary variables and generalized contrastive learning,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.578764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 45284cf3-0553-4120-9fdf-7823d1c72afd · outbound

This paper cites Mujoco: A physics engine for model- based control,.

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

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.432128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 231f4d62-5cee-4d60-b890-520334e26baf · outbound

This paper cites Proximal Policy Optimization Algorithms.

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

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.437034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91d19a9d-e8ec-49b8-af2b-ef3abefebaf2 · outbound

This paper cites Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:36:20.547305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b8a0b7bc-1b84-4683-97c3-a69109487b55 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T20:36:20.447781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers