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Paper Citation Record · LEDGER

Distributional Inverse Reinforcement Learning

As of 11 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 0 inbound Pith citation observations for arXiv:2510.03013.

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

pith.paper-citation-record.v1
2510.03013 v4

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measured 67 of 67 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-04T12:42:58.448782Z

measured 67 of 67 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

67 of 67 outbound references displayed

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Outbound references

Observation 5b5898a2-9e6e-4207-8cff-c45924fdaa44 · outbound

This paper cites write newline.

Distributional Inverse Reinforcement Learning write newline

Reference 1

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Observation 4b8df049-d540-41cd-a937-bab4baf90072 · outbound

This paper cites Apprenticeship learning via inverse reinforcement learning.

Distributional Inverse Reinforcement Learning Apprenticeship learning via inverse reinforcement learning

Reference 2

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Observation 951c8b6d-31ed-4378-b53e-f4530b549d7c · outbound

This paper cites A survey of inverse reinforcement learning: Challenges, methods and progress.

Distributional Inverse Reinforcement Learning A survey of inverse reinforcement learning: Challenges, methods and progress

Reference 3

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Observation dc68dd71-a32f-46d9-9dfa-14bdeae8aa38 · outbound

This paper cites Dynamic inverse reinforcement learning for characterizing animal behavior.

Distributional Inverse Reinforcement Learning Dynamic inverse reinforcement learning for characterizing animal behavior

Reference 4

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Observation 987d6621-cb01-4a7e-8463-08ee09d332c3 · outbound

This paper cites The multivariate skew-normal distribution.

Distributional Inverse Reinforcement Learning The multivariate skew-normal distribution

Reference 5

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Observation e240067a-e277-4e0d-aea1-92a7cebae757 · outbound

This paper cites Walking the Values in Bayesian Inverse Reinforcement Learning.

Distributional Inverse Reinforcement Learning Walking the Values in Bayesian Inverse Reinforcement Learning

Reference 6

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Observation 6d5069a7-bdef-4b9c-95eb-8e9c8b03b40a · outbound

This paper cites A distributional perspective on reinforcement learning.

Distributional Inverse Reinforcement Learning A distributional perspective on reinforcement learning

Reference 7

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Observation a9ca4cd1-a0c0-4c5a-b060-f28417ab564d · outbound

This paper cites Variational inference: A review for statisticians.

Distributional Inverse Reinforcement Learning Variational inference: A review for statisticians

Reference 8

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Observation 3280a2e8-d2b7-4a9e-b2b6-f5f3d768c23a · outbound

This paper cites Scalable Bayesian Inverse Reinforcement Learning.

Distributional Inverse Reinforcement Learning Scalable Bayesian Inverse Reinforcement Learning

Reference 9

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Observation 87d522fa-a973-41ac-8739-c59118afa6d0 · outbound

This paper cites Eliciting risk aversion with inverse reinforcement learning via interactive questioning.

Distributional Inverse Reinforcement Learning Eliciting risk aversion with inverse reinforcement learning via interactive questioning

Reference 10

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Observation 1251fc23-f23c-41da-b5ee-f62ade9979f4 · outbound

This paper cites Map inference for bayesian inverse reinforcement learning.

Distributional Inverse Reinforcement Learning Map inference for bayesian inverse reinforcement learning

Reference 11

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Observation 8c67766b-f540-42ec-9332-be25c24175b9 · outbound

This paper cites Implicit quantile networks for distributional reinforcement learning.

Distributional Inverse Reinforcement Learning Implicit quantile networks for distributional reinforcement learning

Reference 12

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Observation 82372248-f577-43f2-94c3-bff25775f54a · outbound

This paper cites Distributional reinforcement learning with quantile regression.

Distributional Inverse Reinforcement Learning Distributional reinforcement learning with quantile regression

Reference 13

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Observation c0b8e8b4-6128-4a8e-b5c9-9fef05542847 · outbound

This paper cites Cortical substrates for exploratory decisions in humans.

Distributional Inverse Reinforcement Learning Cortical substrates for exploratory decisions in humans

Reference 14

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Observation 05695a72-5de5-4f56-8617-f3a7ef553418 · outbound

This paper cites Nonuniform random variate generation.

Distributional Inverse Reinforcement Learning Nonuniform random variate generation

Reference 15

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Distributional Inverse Reinforcement Learning Remarks on quantiles and distortion risk measures

Reference 16

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Observation 71049599-d292-4904-8b18-aca7577d1680 · outbound

This paper cites Distributional soft actor-critic: Off-policy reinforcement learning for addressing value estimation errors.

Distributional Inverse Reinforcement Learning Distributional soft actor-critic: Off-policy reinforcement learning for addressing value estimation errors

Reference 17

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Observation 6ff1f605-4b91-41a4-928e-48d7f8e0ade7 · outbound

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

Distributional Inverse Reinforcement Learning D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 18

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Observation 191ba8eb-0a53-45de-96b4-86c9b0c801af · outbound

This paper cites Iq-learn: Inverse soft-q learning for imitation.

Distributional Inverse Reinforcement Learning Iq-learn: Inverse soft-q learning for imitation

Reference 19

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Observation d84e4251-f711-41d5-a78a-566f5746e555 · outbound

This paper cites Probability: a graduate course, volume 200.

Distributional Inverse Reinforcement Learning Probability: a graduate course, volume 200

Reference 20

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Observation a9f5bb93-514b-4621-9b9d-60801c06df64 · outbound

This paper cites Rules for ordering uncertain prospects.

Distributional Inverse Reinforcement Learning Rules for ordering uncertain prospects

Reference 21

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Observation c2569331-cc4c-4bc4-9fd4-e6d1bfa48911 · outbound

This paper cites IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies.

Distributional Inverse Reinforcement Learning IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

Reference 22

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Observation bcadbcff-cf03-4c10-884a-67ebf6fe7aca · outbound

This paper cites Introduction to real analysis, volume 280.

Distributional Inverse Reinforcement Learning Introduction to real analysis, volume 280

Reference 23

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Observation 5adf489c-1b76-45a4-b6d5-39d8e54cfbca · outbound

This paper cites Generative adversarial imitation learning.

Distributional Inverse Reinforcement Learning Generative adversarial imitation learning

Reference 24

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Observation 5a7e6729-f9ef-450e-8d2c-57da76d2abf3 · outbound

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Distributional Inverse Reinforcement Learning A bayesian approach to generative adversarial imitation learning

Reference 25

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Observation bb73d1d7-8978-4d1d-b438-4bb3022ba912 · outbound

This paper cites Rize: Regularized imitation learning via distributional reinforcement learning.

Distributional Inverse Reinforcement Learning Rize: Regularized imitation learning via distributional reinforcement learning

Reference 26

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Observation c51944cb-860f-43b7-bdd3-aa00e00e696b · outbound

This paper cites Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors.

Distributional Inverse Reinforcement Learning Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors

Reference 27

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Observation 0eb30226-101e-49d8-b78c-4e8d28c65da1 · outbound

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Distributional Inverse Reinforcement Learning Imitation Learning via Off-Policy Distribution Matching

Reference 28

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Observation 4a2b943b-36dd-43c2-8c59-3d1ea16fc9af · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

Distributional Inverse Reinforcement Learning Offline Reinforcement Learning with Implicit Q-Learning

Reference 29

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Observation 96537912-f523-42aa-9d93-d40580eef4d7 · outbound

This paper cites Risk-sensitive generative adversarial imitation learning.

Distributional Inverse Reinforcement Learning Risk-sensitive generative adversarial imitation learning

Reference 30

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Distributional Inverse Reinforcement Learning A tutorial on energy-based learning

Reference 31

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Observation 6274dfce-ac41-4ba8-970c-be7613c3b777 · outbound

This paper cites Risk-sensitive mpcs with deep distributional inverse rl for autonomous driving.

Distributional Inverse Reinforcement Learning Risk-sensitive mpcs with deep distributional inverse rl for autonomous driving

Reference 32

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Observation 38eaa1e7-0b35-4894-b588-06dcbbc25883 · outbound

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Distributional Inverse Reinforcement Learning Nonlinear inverse reinforcement learning with gaussian processes

Reference 33

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Distributional Inverse Reinforcement Learning Internally rewarded reinforcement learning

Reference 34

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This paper cites Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space.

Distributional Inverse Reinforcement Learning Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space

Reference 35

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This paper cites Distributional reinforcement learning for risk-sensitive policies.

Distributional Inverse Reinforcement Learning Distributional reinforcement learning for risk-sensitive policies

Reference 36

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Observation b1e489fe-d107-4c75-b4da-8e70b1bf39c2 · outbound

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Distributional Inverse Reinforcement Learning Kernel Density Bayesian Inverse Reinforcement Learning

Reference 37

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This paper cites Spontaneous behaviour is structured by reinforcement without explicit reward.

Distributional Inverse Reinforcement Learning Spontaneous behaviour is structured by reinforcement without explicit reward

Reference 38

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Observation fc515a0d-1ff0-4141-97ee-87b63a27c736 · outbound

This paper cites Spontaneous behaviour is structured by reinforcement without explicit reward.

Distributional Inverse Reinforcement Learning Spontaneous behaviour is structured by reinforcement without explicit reward

Reference 39

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Distributional Inverse Reinforcement Learning The kolmogorov-smirnov test for goodness of fit

Reference 40

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Observation 89935910-fef2-4b54-9ee2-bb176b05306b · outbound

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Distributional Inverse Reinforcement Learning Foraging for foundations in decision neuroscience: insights from ethology

Reference 41

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Observation fd7e5dd7-16e3-44a6-8a66-6f40131ed568 · outbound

This paper cites f-irl: Inverse reinforcement learning via state marginal matching.

Distributional Inverse Reinforcement Learning f-irl: Inverse reinforcement learning via state marginal matching

Reference 42

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source=arxiv_source observed=2026-08-04T12:42:58.361919Z digest=sha256:a8587b17bba8ff553dca9376ded1a12ef1efa950039de31edf60a4a072f059f2

Observation 49e421da-f209-41ef-a317-006242c67d6c · outbound

This paper cites Bayesian inverse reinforcement learning.

Distributional Inverse Reinforcement Learning Bayesian inverse reinforcement learning

Reference 43

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Observation d10c9de8-520a-44ea-81d5-ef6ce727d9f7 · outbound

This paper cites Optimization of conditional value-at-risk.

Distributional Inverse Reinforcement Learning Optimization of conditional value-at-risk

Reference 44

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source=arxiv_source observed=2026-08-04T12:42:58.368395Z digest=sha256:53005bf6adc472acf8f7f67f06fe03edd4d20c9d4f125a0866d3746048f04842

Observation 962fc82b-049f-43bb-a3db-974dcc98e7af · outbound

This paper cites Driving with style: Inverse reinforcement learning in general-purpose planning for automated driving.

Distributional Inverse Reinforcement Learning Driving with style: Inverse reinforcement learning in general-purpose planning for automated driving

Reference 45

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source=arxiv_source observed=2026-08-04T12:42:58.372070Z digest=sha256:fa0b3d5e45401336937e55761d482d4c1c2eb1b4ffe5136bd1608664d85a9bdd

Observation bd848cbc-33f6-462e-bffb-0fe808aa7fd6 · outbound

This paper cites Learning risk-aware quadrupedal locomotion using distributional reinforcement learning.

Distributional Inverse Reinforcement Learning Learning risk-aware quadrupedal locomotion using distributional reinforcement learning

Reference 46

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source=arxiv_source observed=2026-08-04T12:42:58.375329Z digest=sha256:011b8b8d9b51a4341bb99869a77d255bf7870033b5672c8af558d31b89975850

Observation d70fa356-7c2a-4479-9e7b-87088047875d · outbound

This paper cites A neural substrate of prediction and reward.

Distributional Inverse Reinforcement Learning A neural substrate of prediction and reward

Reference 47

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source=arxiv_source observed=2026-08-04T12:42:58.378371Z digest=sha256:cf1641cb55ed8283481d7f4c555130df9c4b42f8e5b174b4d7c2093d6342d977

Observation e934e22a-4172-48e9-ad35-ff0ea398474a · outbound

This paper cites Distortion risk measures in portfolio optimization.

Distributional Inverse Reinforcement Learning Distortion risk measures in portfolio optimization

Reference 48

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source=arxiv_source observed=2026-08-04T12:42:58.381868Z digest=sha256:0914c60c13ac4078e572e36d265a2239c725d98551784fdbac07bda7a802682e

Observation f8643bf4-bbad-422a-8b03-dda969f203f9 · outbound

This paper cites Risk-sensitive inverse reinforcement learning via semi-and non-parametric methods.

Distributional Inverse Reinforcement Learning Risk-sensitive inverse reinforcement learning via semi-and non-parametric methods

Reference 49

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source=arxiv_source observed=2026-08-04T12:42:58.385441Z digest=sha256:8422b436a4c2294229f113a5052e6308d6df3f86a59cb0e987fdb11149fb1c03

Observation 694dc24c-9400-4ba9-baa9-422b04a29993 · outbound

This paper cites Reinforcement learning: An introduction, volume 1.

Distributional Inverse Reinforcement Learning Reinforcement learning: An introduction, volume 1

Reference 50

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source=arxiv_source observed=2026-08-04T12:42:58.389074Z digest=sha256:b2a71a95c33f5ba5398e6775db39b83d9fa80fbd1d4c7398ca760b88793a0ab2

Observation ae7feb3d-cf74-45cc-9a8f-b91b17477862 · outbound

This paper cites Risk-Averse Offline Reinforcement Learning.

Distributional Inverse Reinforcement Learning Risk-Averse Offline Reinforcement Learning

Reference 51

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source=arxiv_source observed=2026-08-04T12:42:58.392711Z digest=sha256:4166c961119ae4f93d025537efaf9477d8f2c7970e8195a630c00662605b379b

Observation f284b05b-1aed-4ac9-a934-981632a98f3f · outbound

This paper cites Inverse reinforcement learning algorithms and features for robot navigation in crowds: an experimental comparison.

Distributional Inverse Reinforcement Learning Inverse reinforcement learning algorithms and features for robot navigation in crowds: an experimental comparison

Reference 52

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source=arxiv_source observed=2026-08-04T12:42:58.396647Z digest=sha256:0198f3f93ec136d07c2722e16c552856491d6bd4a7531db649a29387b6ea3905

Observation 619a0652-a124-43d8-ac85-72d12e9b5eed · outbound

This paper cites A bayesian approach to robust inverse reinforcement learning.

Distributional Inverse Reinforcement Learning A bayesian approach to robust inverse reinforcement learning

Reference 53

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source=arxiv_source observed=2026-08-04T12:42:58.399778Z digest=sha256:b0409c797674ecf505d2365057757afabb015041e849ad266408957921620ea1

Observation b54de15a-532a-4048-895d-f47826a0041c · outbound

This paper cites Foundations of multivariate distributional reinforcement learning.

Distributional Inverse Reinforcement Learning Foundations of multivariate distributional reinforcement learning

Reference 54

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Observation ac59373e-e0cd-40cf-a6f2-1b0b3e05b5d1 · outbound

This paper cites Inverse reinforcement learning with the average reward criterion.

Distributional Inverse Reinforcement Learning Inverse reinforcement learning with the average reward criterion

Reference 55

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source=arxiv_source observed=2026-08-04T12:42:58.406388Z digest=sha256:69c10ba4965bdb430f99229706b42e666650480f740709d5e2df54c941a56018

Observation 928c7e42-6f67-4705-b4b8-226eb39208a6 · outbound

This paper cites Infer and adapt: Bipedal locomotion reward learning from demonstrations via inverse reinforcement learning.

Distributional Inverse Reinforcement Learning Infer and adapt: Bipedal locomotion reward learning from demonstrations via inverse reinforcement learning

Reference 56

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source=arxiv_source observed=2026-08-04T12:42:58.410677Z digest=sha256:645d5e1e7bbb7d4c52e10fd21f695d9dbb345b99fbbfd2428ffeb65d36f38891

Observation 21d38730-2082-4924-9641-d4821001d9b2 · outbound

This paper cites Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving.

Distributional Inverse Reinforcement Learning Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving

Reference 57

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source=arxiv_source observed=2026-08-04T12:42:58.414728Z digest=sha256:e075361347002db80bdc654d01c66320669c767b508eba5be404f128aa3c0cc3

Observation be72741b-a6bd-4959-b66a-c2d68dd9bf25 · outbound

This paper cites Maximum Entropy Deep Inverse Reinforcement Learning.

Distributional Inverse Reinforcement Learning Maximum Entropy Deep Inverse Reinforcement Learning

Reference 58

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source=arxiv_source observed=2026-08-04T12:42:58.418262Z digest=sha256:dbb82b50238e9bdcf0b583ba1253162006789a8b7d71995dc1258077ebce6643

Observation 4b45876f-d777-4d36-a0ab-a76728734313 · outbound

This paper cites Modeling, learning, perception, and control methods for deformable object manipulation.

Distributional Inverse Reinforcement Learning Modeling, learning, perception, and control methods for deformable object manipulation

Reference 59

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source=arxiv_source observed=2026-08-04T12:42:58.421838Z digest=sha256:d29e3490d7a5c3bef2704672f5b21ba33de8204482b39fa752f676aefdc26eaa

Observation c6610b64-fb91-4bf9-b361-f978d26a6849 · outbound

This paper cites Maximum-likelihood inverse reinforcement learning with finite-time guarantees.

Distributional Inverse Reinforcement Learning Maximum-likelihood inverse reinforcement learning with finite-time guarantees

Reference 60

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source=arxiv_source observed=2026-08-04T12:42:58.425216Z digest=sha256:49a6c0a580fe41715c5fc772b389ebd6a934ac8ef55aa017398287f259d77bce

Observation 370e9883-f575-4580-9d4d-3c98b22aa114 · outbound

This paper cites When demonstrations meet generative world models: A maximum likelihood framework for offline inverse reinforcement learning.

Distributional Inverse Reinforcement Learning When demonstrations meet generative world models: A maximum likelihood framework for offline inverse reinforcement learning

Reference 61

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source=arxiv_source observed=2026-08-04T12:42:58.428599Z digest=sha256:0914be60bc1c09799882c8f417479a668021382d0a2276d6e77f2f950735798c

Observation d71cef4b-e355-4f82-8641-1f296cff776e · outbound

This paper cites From Demonstrations to Rewards: Alignment Without Explicit Human Preferences.

Distributional Inverse Reinforcement Learning From Demonstrations to Rewards: Alignment Without Explicit Human Preferences

Reference 62

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source=arxiv_source observed=2026-08-04T12:42:58.431814Z digest=sha256:d328f879650c577773cf6eb7b689df58bca9b366aa7fc7862f39e42e7b4e5fd0

Observation 41948553-1110-4682-9151-3bb609fa59b0 · outbound

This paper cites Maximum entropy inverse reinforcement learning.

Distributional Inverse Reinforcement Learning Maximum entropy inverse reinforcement learning

Reference 63

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source=arxiv_source observed=2026-08-04T12:42:58.435351Z digest=sha256:03081b0a900a828d1acf058f473db217b6d255024af9526ed0af5f6d758108c1

Observation b22c4cda-33a9-4827-a484-d24b276a9057 · outbound

This paper cites Modeling interaction via the principle of maximum causal entropy.

Distributional Inverse Reinforcement Learning Modeling interaction via the principle of maximum causal entropy

Reference 64

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source=arxiv_source observed=2026-08-04T12:42:58.438443Z digest=sha256:e1360666098465e74a7059947dc6ba258839dc523f6a8cdaea6c025aec104265

Observation ee23053d-dee5-4620-84c1-22874c44a4ba · outbound

This paper cites @esa (Ref.

Distributional Inverse Reinforcement Learning @esa (Ref

Reference 65

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source=arxiv_source observed=2026-08-04T12:42:58.441299Z digest=sha256:fc58ec4d9b4b86118d1f64adabcc0f1efe6e34b11ddb432e67870ca5e6738aa9

Observation d7ffbf34-29ce-49cd-8d9f-9c684f698892 · outbound

This paper cites an unresolved cited work.

Distributional Inverse Reinforcement Learning Unresolved cited work

Reference 66

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source=arxiv_source observed=2026-08-04T12:42:58.445479Z digest=sha256:24a8dd267a494b4e9caa39d2562248704eb1a8990803ae067071176acab326a3

Observation ac7d397b-a0ae-4487-a0cf-968152f5c08f · outbound

This paper cites an unresolved cited work.

Distributional Inverse Reinforcement Learning Unresolved cited work

Reference 67

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source=arxiv_source observed=2026-08-04T12:42:58.448782Z digest=sha256:79b2fdded98c5fd228c2f7986ea382f7b53ad9ef3db9c5dd07fa3b7822da44f1

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