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Paper Citation Record · LEDGER

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach

As of 20 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2505.06482.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.06482 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:46:53.835259Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

23 of 23 outbound references displayed

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  • verified fuzzy6
  • unresolved16
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 796b0fab-28e7-4e62-a983-6d8e65473048 · outbound

This paper cites Each dataset consists of 200 trajectories, each with 500 time steps.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Each dataset consists of 200 trajectories, each with 500 time steps

Reference 1

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Observation 56ec6d04-3fa2-437b-8544-fca2a0dc4914 · outbound

This paper cites an unresolved cited work.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Unresolved cited work

Reference 2

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Observation 1f3bd618-e74d-49b4-aca4-09d0d1738174 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 4

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Observation a23675d9-727e-4746-9944-0e676ac289ce · outbound

This paper cites Mastering Diverse Domains through World Models.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Mastering Diverse Domains through World Models

Reference 6

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Observation edb9085c-d3c5-49da-92c9-ff6dca96b0ff · outbound

This paper cites Handle Press w/ MMD loss w/o MMD loss DreamerV2 Episode return 2651± 620 1961 ± 585 1202 ± 422 Success rate 0.60± 0.12 0.45 ± 0.15 0.33 ± 0.11 W/ MMD loss W/o MMD loss Figure.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Handle Press w/ MMD loss w/o MMD loss DreamerV2 Episode return 2651± 620 1961 ± 585 1202 ± 422 Success rate 0.60± 0.12 0.45 ± 0.15 0.33 ± 0.11 W/ MMD loss W/o MMD loss Figure

Reference 7

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Observation ac7c549c-ee90-495f-97e9-17f11ec6b861 · outbound

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

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 8

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Observation 225319e4-b757-4d5b-9c4f-323ee05c7d7d · outbound

This paper cites Proximal Policy Optimization Algorithms.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Proximal Policy Optimization Algorithms

Reference 11

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Observation c1b438e2-2fe5-4c02-b0f5-68cf622ef4e5 · outbound

This paper cites Offline Reinforcement Learning for Visual Navigation.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Offline Reinforcement Learning for Visual Navigation

Reference 12

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Observation f0e71377-c170-4643-a7c2-ab4c4efdd6f1 · outbound

This paper cites Model-Based Visual Planning with Self-Supervised Functional Distances.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Model-Based Visual Planning with Self-Supervised Functional Distances

Reference 13

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Observation ef56a52a-c905-4ff0-a88b-4c82b3d112c1 · outbound

This paper cites Behavior Regularized Offline Reinforcement Learning.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Behavior Regularized Offline Reinforcement Learning

Reference 15

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Observation 566ca729-ebd6-4c8e-9646-c519be0063c6 · outbound

This paper cites Offline visual representation learning for embodied nav- igation.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Offline visual representation learning for embodied nav- igation

Reference 16

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Observation 0aa81737-1e58-414b-afa1-f080b49642fc · outbound

This paper cites Playable Game Generation.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Playable Game Generation

Reference 17

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Observation 567e03dc-bd71-473e-8d99-e41f87769bbd · outbound

This paper cites an unresolved cited work.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Unresolved cited work

Reference 20

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

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Observation e6cb964e-91c8-4208-ab55-3ed58e643bb0 · outbound

This paper cites As the number of source domain videos increases, the model’s performance improves accordingly.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach As the number of source domain videos increases, the model’s performance improves accordingly

Reference 21

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Observation c2802aea-4e39-4ef7-b428-cfdd6e7e29e0 · outbound

This paper cites • LOMPO (Rafailov et al., 2021): A model-based offline visual RL method that addresses model uncertainty in the latent space while incorporating explicit reward estimation.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach • LOMPO (Rafailov et al., 2021): A model-based offline visual RL method that addresses model uncertainty in the latent space while incorporating explicit reward estimation

Reference 22

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

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Observation 4762985b-b92c-4635-83c6-c0949906a71c · outbound

This paper cites nuScenes: A multimodal dataset for autonomous driving.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach nuScenes: A multimodal dataset for autonomous driving

Reference 2006

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Observation 713f28c6-9624-4377-8efc-1676b708e9d0 · outbound

This paper cites Diffusion Models Are Real-Time Game Engines.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Diffusion Models Are Real-Time Game Engines

Reference 2018

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Observation 978c59c0-3aca-4c3a-b943-f82c650631f1 · outbound

This paper cites Learning from Sparse Offline Datasets via Conservative Density Estimation.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Learning from Sparse Offline Datasets via Conservative Density Estimation

Reference 2019

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Observation 04676327-30e0-4528-8a1b-53c363d355ab · outbound

This paper cites Crafting papers on machine learning.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Crafting papers on machine learning

Reference 2020

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Observation 7d5256dd-200c-4d66-972a-9145d8d214a6 · outbound

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

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Off-policy deep reinforcement learning without exploration

Reference 2021

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Observation 00032239-c86c-43d4-9920-87e7afb5804b · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 2022

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Observation 2ae20b2a-9e34-4bfa-91cb-95ae38450133 · outbound

This paper cites R3M: A Universal Visual Representation for Robot Manipulation.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach R3M: A Universal Visual Representation for Robot Manipulation

Reference 2023

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Observation 361532cc-98cb-4f36-ab05-2ccda35954e1 · outbound

This paper cites Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning.

Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 2024

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