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Paper Citation Record · LEDGER

Recursive Deep Inverse Reinforcement Learning

As of 17 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2504.13241.

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

pith.paper-citation-record.v1
2504.13241 v6

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:20:03.392082Z

measured 15 of 15 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

15 of 15 outbound references displayed

  • verified exact1
  • verified fuzzy5
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f244a49c-5dc1-4a4d-b9be-0e79f88fac94 · outbound

This paper cites The goal is to iteratively updateθ such that trajectories generated from the current policyq(τ) match the expert demonstrations.

Recursive Deep Inverse Reinforcement Learning The goal is to iteratively updateθ such that trajectories generated from the current policyq(τ) match the expert demonstrations

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:20:04.011525Z

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.

source=pdf_text observed=2026-08-16T12:20:03.392082Z digest=sha256:5fb193d109e217ae4b9195fbe21bb93c0bb0ee7e22089d2e6fd0ca36dad0a916

Observation b186050c-96f6-4818-a2fb-477cacb507f6 · outbound

This paper cites an unresolved cited work.

Recursive Deep Inverse Reinforcement Learning Unresolved cited work

Reference 3

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unresolved
raw_fallback, observed 2026-08-16T12:20:04.061660Z

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.

source=pdf_text observed=2026-08-16T12:20:03.381251Z digest=sha256:aa28545743ff410fed55a99a83eed09f2faa05e76b13cae66d8da9839366ba01

Observation a896ca1d-1c40-46bb-90d6-897b82d2ee12 · outbound

This paper cites Jeffrey Humpherys, Preston Redd, and Jeremy West.

Recursive Deep Inverse Reinforcement Learning Jeffrey Humpherys, Preston Redd, and Jeremy West

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-16T12:20:04.272218Z

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.

source=pdf_text observed=2026-08-16T12:20:03.238615Z digest=sha256:cde5212bac1422d6abe63e5d6ec17a7ad72f18ce68ab2fd1d6888d571d8ff30b

Observation 78a9469f-c463-45c0-bebf-e411e66ddebd · outbound

This paper cites Online inverse reinforcement learning for systems with disturbances.

Recursive Deep Inverse Reinforcement Learning Online inverse reinforcement learning for systems with disturbances

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-16T12:20:04.167196Z

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.

source=pdf_text observed=2026-08-16T12:20:03.300432Z digest=sha256:b8a21b90ed48d71ffaf7b915dc34b3992a98859ce1b406bfaa23a1f326c2af82

Observation afa43436-d8ea-44fc-b3ac-f0fb70a626ef · outbound

This paper cites MPPIis a probabilistic model predictive control policy that estimates an optimal action distribution that minimizes an agent’s objective cost function.

Recursive Deep Inverse Reinforcement Learning MPPIis a probabilistic model predictive control policy that estimates an optimal action distribution that minimizes an agent’s objective cost function

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-16T12:20:04.113106Z

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.

source=pdf_text observed=2026-08-16T12:20:03.372490Z digest=sha256:745235bcd46a9e9a743065bfcc1514c573f559116021bc93ce007bf7c270751e

Observation ab6a0f7b-7ddd-4060-88fd-aca3bcde2b6e · outbound

This paper cites SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards.

Recursive Deep Inverse Reinforcement Learning SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

Reference 2006

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unresolved
no resolver link, observed 2026-08-16T12:20:03.259640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:20:03.259640Z digest=sha256:f1e808221e62a7577bd193967ecd408f77cd7502910abc36412bff9ef515501e

Observation 763c74e1-7e11-4641-a20f-acd65dd55736 · outbound

This paper cites OpenAI Gym.

Recursive Deep Inverse Reinforcement Learning OpenAI Gym

Reference 2011

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unresolved
no resolver link, observed 2026-08-16T12:20:03.198564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:20:03.198564Z digest=sha256:ab8fb0f3463544299396bbef1e30c7f749e2eb052d8c6f159806a9283649865f

Observation 4ec0c19e-d124-4f08-8fed-fcef40dea3cc · outbound

This paper cites Continuously Optimizing Radar Placement with Model Predictive Path Integrals.

Recursive Deep Inverse Reinforcement Learning Continuously Optimizing Radar Placement with Model Predictive Path Integrals

Reference 2014

Resolution
verified exact
local_arxiv, observed 2026-08-16T12:20:03.744727Z

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.

source=pdf_text observed=2026-08-16T12:20:03.247932Z digest=sha256:402e166b8302b7218a59e74f0960f66dd94a97f2ae5c1fcb4716b59e00d4aec6

Observation 5b5497a5-de0e-4429-a024-8ca0c69be37c · outbound

This paper cites A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models.

Recursive Deep Inverse Reinforcement Learning A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-16T12:20:03.214955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:20:03.214955Z digest=sha256:a27e352b2d3ae91a3747e3da774424a3772c77d99ccaae39e962743df4afbb4e

Observation 8b9a2569-c9b5-45c8-8e23-301191a6ae42 · outbound

This paper cites From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following.

Recursive Deep Inverse Reinforcement Learning From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following

Reference 2017

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unresolved
no resolver link, observed 2026-08-16T12:20:03.226414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:20:03.226414Z digest=sha256:d1e9e76d896566b31526874ce58e1cd54a1455ab40d93c2f2fefa919312370fb

Observation d00be9b5-f411-44d4-be34-bffd590e94b2 · outbound

This paper cites Bayesian estimation and kalman filtering: A unified framework for mobile robot localization.

Recursive Deep Inverse Reinforcement Learning Bayesian estimation and kalman filtering: A unified framework for mobile robot localization

Reference 2018

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:20:04.214465Z

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.

source=pdf_text observed=2026-08-16T12:20:03.277335Z digest=sha256:e834c03791eb0bc8342ee1f468ae707ed61ddaa0e74dc84c5f9ee0c5b77074e6

Observation 872db340-bb6d-481a-98c6-82171a8b552d · outbound

This paper cites Maximum Entropy Deep Inverse Reinforcement Learning.

Recursive Deep Inverse Reinforcement Learning Maximum Entropy Deep Inverse Reinforcement Learning

Reference 2020

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unresolved
no resolver link, observed 2026-08-16T12:20:03.334151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:20:03.334151Z digest=sha256:bdda04e783880cf79900798903eeac329be3bcbb05c10ce24672132e9cbda634

Observation 62a473f0-cdfb-49b9-b22f-7ca461fab91d · outbound

This paper cites Derivation of a Constant Velocity Motion Model for Visual Tracking.

Recursive Deep Inverse Reinforcement Learning Derivation of a Constant Velocity Motion Model for Visual Tracking

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-16T12:20:03.188403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:20:03.188403Z digest=sha256:6e0a8419b86e570953809b946286c38ed26ea3547840989b74e7819e202c6ac0

Observation ae80fe38-cc57-4aab-8eac-fed48d5e2578 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Recursive Deep Inverse Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 2023

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unresolved
no resolver link, observed 2026-08-16T12:20:03.288316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:20:03.288316Z digest=sha256:52dbe84939b452e6f2852a31d19843c86c1f7489a4013482c30dc4f7928c14c6

Observation 73e85a81-d18d-4361-b0b0-d1ecc783786d · outbound

This paper cites Extended Kalman Filtering for Recursive Online Discrete-Time Inverse Optimal Control.

Recursive Deep Inverse Reinforcement Learning Extended Kalman Filtering for Recursive Online Discrete-Time Inverse Optimal Control

Reference 2025

Resolution
metadata mismatch
local_arxiv, observed 2026-08-16T12:20:03.513358Z

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.

source=pdf_text observed=2026-08-16T12:20:03.346064Z digest=sha256:8719b71d60673d20e2b19bdcd6c299360fe2b5038a05477c4dbe3fe17bdc3d95

Pith citing papers

No inbound Pith citation observations are available.