Paper Citation Record · LEDGER

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance

As of 17 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2504.18766.

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

pith.paper-citation-record.v1
2504.18766 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:14:54.453290Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy10
unresolved29
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External citation measurements

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Outbound references

Observation f4617197-167f-4087-a297-6580c6f3e361 · outbound

This paper cites Behavior priors for efficient reinforcement learning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Behavior priors for efficient reinforcement learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:14:55.266723Z

Source-reported events for the cited work

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Observation 34cd9837-379f-4b9c-89f0-61985a43107d · outbound

This paper cites Deep q-learning from demonstrations.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Deep q-learning from demonstrations

Reference 2

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Observation 5b6da731-1ffe-47b6-9e27-5b82021330b9 · outbound

This paper cites Policy optimization with demonstrations.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Policy optimization with demonstrations

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:14:55.242951Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4ef7c341-1120-4948-ac00-da056b930675 · outbound

This paper cites Overcoming Exploration in Reinforcement Learning with Demonstrations.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Overcoming Exploration in Reinforcement Learning with Demonstrations

Reference 4

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Observation 36574b8c-082d-43e2-8c70-50bfa9d31dff · outbound

This paper cites Making Efficient Use of Demonstrations to Solve Hard Exploration Problems.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Making Efficient Use of Demonstrations to Solve Hard Exploration Problems

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.293113Z

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Observation 40fa7fe9-2f8d-41fe-bcb6-ab4b0843947a · outbound

This paper cites Shaping rewards for reinforcement learn- ing with imperfect demonstrations using generative models.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Shaping rewards for reinforcement learn- ing with imperfect demonstrations using generative models

Reference 6

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Source-reported events for the cited work

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Observation 863b70f7-1509-4a93-a694-073bc01ba358 · outbound

This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.303847Z

Source-reported events for the cited work

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Observation 6905ef46-0e3e-47ce-ba22-409ba4d27985 · outbound

This paper cites Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:14:55.229083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7119433e-63c7-4298-b95b-6aefad78ae80 · outbound

This paper cites Residual Reinforcement Learning for Robot Control.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Residual Reinforcement Learning for Robot Control

Reference 9

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Observation da529b81-927c-4923-bf64-33a7f839ca3f · outbound

This paper cites Blending Imitation and Reinforcement Learning for Robust Policy Improvement.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Blending Imitation and Reinforcement Learning for Robust Policy Improvement

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.316566Z

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Observation aafdd562-9db4-4a11-9b1f-a998429c3d3d · outbound

This paper cites Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.320971Z

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Observation 6fd32781-7de5-41c1-8e7a-a9218de8a13b · outbound

This paper cites Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.325230Z

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Observation 67b536a4-7de8-4665-a745-256f207762e4 · outbound

This paper cites Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.329302Z

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Observation 6025f4a1-3a8e-473c-82c0-c882325869c0 · outbound

This paper cites Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.333775Z

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Unavailable: canonical work link unavailable.

Observation 53c99f43-2e76-4a79-b6f1-d858f2e8c086 · outbound

This paper cites Improving TD3-BC: Relaxed Policy Constraint for Offline Learning and Stable Online Fine-Tuning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Improving TD3-BC: Relaxed Policy Constraint for Offline Learning and Stable Online Fine-Tuning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.338344Z

Source-reported events for the cited work

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Observation 5e332916-4a8d-44bd-94fd-303d6541c71e · outbound

This paper cites Online decision transformer.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Online decision transformer

Reference 16

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Observation b8297af3-273c-4e96-a3a0-ff321d74c71b · outbound

This paper cites COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.347894Z

Source-reported events for the cited work

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Observation 8e0c9724-8e88-4e2c-9768-67d9b35ed723 · outbound

This paper cites SMART: Self-supervised Multi-task pretrAining with contRol Transformers.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance SMART: Self-supervised Multi-task pretrAining with contRol Transformers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.352681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bb942ba-3794-4427-87e2-f55fcd3e96c4 · outbound

This paper cites Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.357538Z

Source-reported events for the cited work

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Observation 3c10010b-509a-4753-8cf0-9a5a861f0bad · outbound

This paper cites Residual Reinforcement Learning from Demonstrations.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Residual Reinforcement Learning from Demonstrations

Reference 20

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Source-reported events for the cited work

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Observation 68f16692-46d8-4506-88cc-bd8c834709aa · outbound

This paper cites Residual learning from demonstration: Adapting dmps for contact- rich manipulation.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Residual learning from demonstration: Adapting dmps for contact- rich manipulation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.365995Z

Source-reported events for the cited work

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Observation 223a9ce2-b5a4-4816-8446-28cee70bb27f · outbound

This paper cites How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.369813Z

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Observation 908a6351-5a98-46d4-8104-5e1650dbb90e · outbound

This paper cites A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regret.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regret

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-16T10:14:54.636203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6daf6545-cc68-4948-8d54-388426735fff · outbound

This paper cites Mix&Match - Agent Curricula for Reinforcement Learning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Mix&Match - Agent Curricula for Reinforcement Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.379501Z

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Observation 9e30ddfa-ad8f-4ac9-afc2-59595473dcbe · outbound

This paper cites Curriculum offline imitating learning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Curriculum offline imitating learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:14:55.206324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 803151ae-a3d8-48ea-9ce0-f5ae9698cb6e · outbound

This paper cites Efficient reductions for imitation learning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Efficient reductions for imitation learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:14:55.191561Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f51beeca-4455-420f-bf38-cc652693a954 · outbound

This paper cites Andrew Bagnell, and Byron Boots.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Andrew Bagnell, and Byron Boots

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:14:55.176684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 511b9796-2809-4fce-ad72-ab38789993b7 · outbound

This paper cites Minimax Optimal Online Imitation Learning via Replay Estimation.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Minimax Optimal Online Imitation Learning via Replay Estimation

Reference 28

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Observation d6510b98-d9d2-47d7-86e3-6da80d8cf57e · outbound

This paper cites Hybrid Inverse Reinforcement Learning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Hybrid Inverse Reinforcement Learning

Reference 29

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Observation 72738d34-8191-4add-8172-64b87ca13f58 · outbound

This paper cites Deep reinforcement learning that matters.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Deep reinforcement learning that matters

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:14:55.161626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 55e0e295-bb48-4232-9e43-45519dd61148 · outbound

This paper cites The Mirage of Action-Dependent Baselines in Reinforcement Learning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance The Mirage of Action-Dependent Baselines in Reinforcement Learning

Reference 31

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Observation 76df687c-b747-47bb-a911-6ea3f7a8c79d · outbound

This paper cites Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 32

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Observation 1a0f3ae6-d2d7-4194-9f6d-698e1499cec5 · outbound

This paper cites What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

Reference 33

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Observation 313d14f8-bc2a-48f7-81d4-a4a9c375226a · outbound

This paper cites Behavior Regularized Offline Reinforcement Learning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Behavior Regularized Offline Reinforcement Learning

Reference 34

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Observation 2068defa-6aff-4fa9-8d44-9889a73797d1 · outbound

This paper cites A minimalist approach to offline reinforcement learning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance A minimalist approach to offline reinforcement learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.427068Z

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Observation 35372177-d14e-4d48-9819-247143aa85b5 · outbound

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

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 36

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Observation b539be7f-340c-4618-b4d3-da8997ab1df9 · outbound

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

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Conservative q-learning for offline reinforcement learning

Reference 37

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Observation ed82b5ed-059e-480a-8e01-42ea275b414c · outbound

This paper cites Addressing function approximation error in actor-critic methods.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Addressing function approximation error in actor-critic methods

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:14:55.128328Z

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Observation d3925d08-ee9d-48e7-a5cf-c9b2d67e674e · outbound

This paper cites A framework for behavioural cloning.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance A framework for behavioural cloning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:14:55.114686Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 32ae6160-3b58-4e9c-a8cb-73b250fc5e73 · outbound

This paper cites Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.453290Z

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Observation ecf8ff24-7d47-4cba-8096-cf371fa7992f · outbound

This paper cites ISBN 0198538677.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance ISBN 0198538677

Reference 1999

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:14:55.101298Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

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