Paper Citation Record · LEDGER

Recursive Deep Inverse Reinforcement Learning

As of 19 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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+00:00.

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

This paper cites an unresolved cited work.

Recursive Deep Inverse Reinforcement Learning Unresolved cited work

Reference 3

Resolution: 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 2026-08-19T06:32:44.657259+00:00.

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

Resolution: 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 2026-08-19T06:32:44.657259+00:00.

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

Resolution: 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 2026-08-19T06:32:44.657259+00:00.

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

Resolution: 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 2026-08-19T06:32:44.657259+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites OpenAI Gym.

Recursive Deep Inverse Reinforcement Learning OpenAI Gym

Reference 2011

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-19T06:32:44.657259+00:00.

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.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-19T06:32:44.657259+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.