Paper Citation Record · LEDGER

Inverting the Bellman Equation: From $Q$-Values to World Models

As of 13 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 1 inbound Pith citation observation for arXiv:2606.21173.

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

pith.paper-citation-record.v1
2606.21173 v1

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T14:33:45.062290Z

measured 73 of 73 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T10:53:30.636580Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T10:53:30.685900Z

Reference resolution

72 of 72 outbound references displayed

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Outbound references

Observation 9c0c9c84-7e6a-4b7e-917b-38e6704b0f3c · outbound

This paper cites A Bradford Book, 2018.

Inverting the Bellman Equation: From $Q$-Values to World Models A Bradford Book, 2018

Reference 1

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Observation 81131771-6cd7-466e-a130-1b899e17dedc · outbound

This paper cites Playing atari with deep reinforcement learning, 2013.

Inverting the Bellman Equation: From $Q$-Values to World Models Playing atari with deep reinforcement learning, 2013

Reference 2

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Observation 74cd8c33-8e89-424d-8832-11e11e39b56c · outbound

This paper cites Proximal Policy Optimization Algorithms, August 2017.

Inverting the Bellman Equation: From $Q$-Values to World Models Proximal Policy Optimization Algorithms, August 2017

Reference 3

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Observation bc689de5-4d55-440c-83a2-2244dd7ec3b5 · outbound

This paper cites Objective mismatch in model-based reinforcement learning.

Inverting the Bellman Equation: From $Q$-Values to World Models Objective mismatch in model-based reinforcement learning

Reference 4

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Observation 122e52c0-426f-473c-ac9f-e627632e6dfb · outbound

This paper cites Moerland, Joost Broekens, Aske Plaat, and Catholijn M.

Inverting the Bellman Equation: From $Q$-Values to World Models Moerland, Joost Broekens, Aske Plaat, and Catholijn M

Reference 5

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Observation d334b879-100e-4bf1-bb98-5beb0c123820 · outbound

This paper cites The value equivalence principle for model-based reinforcement learning.

Inverting the Bellman Equation: From $Q$-Values to World Models The value equivalence principle for model-based reinforcement learning

Reference 6

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Observation d668f6f7-5eea-44f6-8546-1b79f367fbe5 · outbound

This paper cites Proper value equivalence.

Inverting the Bellman Equation: From $Q$-Values to World Models Proper value equivalence

Reference 7

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Observation 519d18ca-06a7-4ecf-ad97-cea46f508357 · outbound

This paper cites Hunt, Tom Schaul, Hado van Hasselt, and David Silver.

Inverting the Bellman Equation: From $Q$-Values to World Models Hunt, Tom Schaul, Hado van Hasselt, and David Silver

Reference 8

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Observation 005ca6f2-3c7f-4a4b-89ab-0a5cd885dad0 · outbound

This paper cites Universal Successor Features Approximators, 2018.

Inverting the Bellman Equation: From $Q$-Values to World Models Universal Successor Features Approximators, 2018

Reference 9

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Observation bb126d42-7c83-4ad2-b09c-d9fbfe1cdcb9 · outbound

This paper cites Learning one representation to optimize all rewards.

Inverting the Bellman Equation: From $Q$-Values to World Models Learning one representation to optimize all rewards

Reference 10

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Observation 128eb90c-0ed5-4ace-be32-872cdbfd9b6e · outbound

This paper cites Universal Value Function Approximators.

Inverting the Bellman Equation: From $Q$-Values to World Models Universal Value Function Approximators

Reference 11

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Observation 7820cceb-8591-4f88-9b39-2c1a50e8839a · outbound

This paper cites Contrastive learning as goal-conditioned reinforcement learning.

Inverting the Bellman Equation: From $Q$-Values to World Models Contrastive learning as goal-conditioned reinforcement learning

Reference 12

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Observation a6e5f7e2-f3c4-4781-b9cf-2de5dfe764b4 · outbound

This paper cites OGBench: Benchmarking Offline Goal-Conditioned RL.

Inverting the Bellman Equation: From $Q$-Values to World Models OGBench: Benchmarking Offline Goal-Conditioned RL

Reference 13

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Observation ce715326-06e5-4007-a167-780717f6ce3e · outbound

This paper cites Unifying task specification in reinforcement learning.

Inverting the Bellman Equation: From $Q$-Values to World Models Unifying task specification in reinforcement learning

Reference 14

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Observation 97e17cb7-3506-4c1d-ad16-a43d152a0063 · outbound

This paper cites Neural fitted q iteration – first experiences with a data efficient neural reinforcement learning method.

Inverting the Bellman Equation: From $Q$-Values to World Models Neural fitted q iteration – first experiences with a data efficient neural reinforcement learning method

Reference 15

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Observation 2957c0e4-d6b4-406c-8d3f-59d7b0a47f46 · outbound

This paper cites Learning to predict by the method of temporal differences.Machine Learning, 08 1988.

Inverting the Bellman Equation: From $Q$-Values to World Models Learning to predict by the method of temporal differences.Machine Learning, 08 1988

Reference 16

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Observation fd1d2be5-83bc-4b6f-b2de-a45c0fdca0f7 · outbound

This paper cites PhD thesis, King’s College, University of Cambridge, Cambridge, UK, May 1989.

Inverting the Bellman Equation: From $Q$-Values to World Models PhD thesis, King’s College, University of Cambridge, Cambridge, UK, May 1989

Reference 17

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Observation e68f5ebf-fdfa-4130-83ac-0dc692dfd26a · outbound

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Inverting the Bellman Equation: From $Q$-Values to World Models Unresolved cited work

Reference 18

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Observation 879ab0d5-1251-40ec-ab68-647efe7a1861 · outbound

This paper cites Rusu, Joel Veness, Marc G.

Inverting the Bellman Equation: From $Q$-Values to World Models Rusu, Joel Veness, Marc G

Reference 19

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Observation 3219933a-2c85-49b7-ae1c-837a6dd5e5e6 · outbound

This paper cites Simplifying deep temporal difference learning.The International Conference on Learning Representations, 2025.

Inverting the Bellman Equation: From $Q$-Values to World Models Simplifying deep temporal difference learning.The International Conference on Learning Representations, 2025

Reference 20

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Observation 63d40f28-ca1e-4e9e-9fc5-4893257c5e2d · outbound

This paper cites Kingma and Max Welling.

Inverting the Bellman Equation: From $Q$-Values to World Models Kingma and Max Welling

Reference 21

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Observation 785a5b29-3f86-4049-a568-8c0b8b98b258 · outbound

This paper cites Stochastic backpropagation and ap- proximate inference in deep generative models.

Inverting the Bellman Equation: From $Q$-Values to World Models Stochastic backpropagation and ap- proximate inference in deep generative models

Reference 22

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Observation 848a1fd0-9be4-4a18-af48-110b9cf637c0 · outbound

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Inverting the Bellman Equation: From $Q$-Values to World Models Unresolved cited work

Reference 23

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Observation f7541e80-d50d-4dc5-a979-9cb878e770f3 · outbound

This paper cites A Stochastic Approximation Method.The Annals of Mathematical Statistics, 1951.

Inverting the Bellman Equation: From $Q$-Values to World Models A Stochastic Approximation Method.The Annals of Mathematical Statistics, 1951

Reference 24

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Observation c70d7c0a-f68a-4afe-976a-8ae19842298c · outbound

This paper cites Rechenberg.

Inverting the Bellman Equation: From $Q$-Values to World Models Rechenberg

Reference 25

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Observation 2db33a3d-7dba-47f2-b039-dde9272565a3 · outbound

This paper cites Evolution strategies at the hyperscale, 2026.

Inverting the Bellman Equation: From $Q$-Values to World Models Evolution strategies at the hyperscale, 2026

Reference 26

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Observation 84c12722-ab2a-4c4f-8227-757d1075e909 · outbound

This paper cites Accelerating Goal-Conditioned RL Algorithms and Research.

Inverting the Bellman Equation: From $Q$-Values to World Models Accelerating Goal-Conditioned RL Algorithms and Research

Reference 27

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Observation b0280a20-519b-4f25-b94d-6a2a5471049a · outbound

This paper cites A single goal is all you need: Skills and exploration emerge from contrastive RL without rewards, demonstrations, or subgoals.

Inverting the Bellman Equation: From $Q$-Values to World Models A single goal is all you need: Skills and exploration emerge from contrastive RL without rewards, demonstrations, or subgoals

Reference 28

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Observation b7639372-2e22-4f3a-80b1-afca7f6e9c21 · outbound

This paper cites Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint, 2021.

Inverting the Bellman Equation: From $Q$-Values to World Models Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint, 2021

Reference 29

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Observation 70ddef7a-4c63-477c-a2ab-5e51e9881f7a · outbound

This paper cites Bellemare, and Hugo Larochelle.

Inverting the Bellman Equation: From $Q$-Values to World Models Bellemare, and Hugo Larochelle

Reference 30

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Observation 0f6f24d3-f414-4a1c-ae73-de512dc51418 · outbound

This paper cites Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M.

Inverting the Bellman Equation: From $Q$-Values to World Models Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M

Reference 31

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Observation 7bd775ff-b075-4190-a401-76b9e470a5b0 · outbound

This paper cites Gymnasium: A standard interface for reinforcement learning environments.https://gymnasium.farama.org/, 2023.

Inverting the Bellman Equation: From $Q$-Values to World Models Gymnasium: A standard interface for reinforcement learning environments.https://gymnasium.farama.org/, 2023

Reference 32

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Observation 36b53a4c-9e46-47e3-ad5c-1e2153589d3c · outbound

This paper cites Mujoco: A physics engine for model-based control.

Inverting the Bellman Equation: From $Q$-Values to World Models Mujoco: A physics engine for model-based control

Reference 33

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Observation cd5f22a2-a8bd-4279-a68c-29c8d1eb0f58 · outbound

This paper cites Sutton, Doina Precup, and Satinder Singh.

Inverting the Bellman Equation: From $Q$-Values to World Models Sutton, Doina Precup, and Satinder Singh

Reference 34

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Observation f88be73a-d308-4cb2-aaab-79b7080c8c5b · outbound

This paper cites Hindsight experience replay.

Inverting the Bellman Equation: From $Q$-Values to World Models Hindsight experience replay

Reference 35

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Observation 4debaaa5-36f4-4c2e-9f22-0ca2bdd229dc · outbound

This paper cites Kahrs, Carlo Sferrazza, Yuval Tassa, and Pieter Abbeel.

Inverting the Bellman Equation: From $Q$-Values to World Models Kahrs, Carlo Sferrazza, Yuval Tassa, and Pieter Abbeel

Reference 36

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Observation 4770d060-1ddc-4999-b07e-f5bff90448a1 · outbound

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Inverting the Bellman Equation: From $Q$-Values to World Models Unresolved cited work

Reference 37

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Observation d38a868d-782d-4afe-9b45-a1ed46f13a9a · outbound

This paper cites 1000 layer networks for self-supervised RL: Scaling depth can enable new goal-reaching capabilities.

Inverting the Bellman Equation: From $Q$-Values to World Models 1000 layer networks for self-supervised RL: Scaling depth can enable new goal-reaching capabilities

Reference 38

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Observation e5ac2edc-7520-4d53-8ba9-2481521bb5a5 · outbound

This paper cites Mastering Atari, Go, chess and shogi by planning with a learned model.Nature, 2020.

Inverting the Bellman Equation: From $Q$-Values to World Models Mastering Atari, Go, chess and shogi by planning with a learned model.Nature, 2020

Reference 39

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Observation 717f200f-b0b0-4ff9-823d-2eb79e8249bd · outbound

This paper cites Iterative value-aware model learning.

Inverting the Bellman Equation: From $Q$-Values to World Models Iterative value-aware model learning

Reference 40

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Observation 2fd6871c-ab87-49f3-ae5e-baf278c05d45 · outbound

This paper cites Unifying model-based and model-free reinforcement learning with equivalent policy sets.

Inverting the Bellman Equation: From $Q$-Values to World Models Unifying model-based and model-free reinforcement learning with equivalent policy sets

Reference 41

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Observation 92e83298-34b2-4ee6-9e8f-cae0110e3a44 · outbound

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Inverting the Bellman Equation: From $Q$-Values to World Models Bayesian exploration networks

Reference 42

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Observation 233e85fe-fa6b-412d-9323-47f437aa95e5 · outbound

This paper cites Strehl, Lihong Li, Eric Wiewiora, John Langford, and Michael L.

Inverting the Bellman Equation: From $Q$-Values to World Models Strehl, Lihong Li, Eric Wiewiora, John Langford, and Michael L

Reference 43

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Observation 2dbfed0c-fcba-449b-a66b-1e930af5f998 · outbound

This paper cites On representation complexity of model-based and model-free reinforcement learning.

Inverting the Bellman Equation: From $Q$-Values to World Models On representation complexity of model-based and model-free reinforcement learning

Reference 44

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Observation e1434e72-7a61-4836-b0af-277d9b9e4d23 · outbound

This paper cites Learning to achieve goals.

Inverting the Bellman Equation: From $Q$-Values to World Models Learning to achieve goals

Reference 45

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Observation fb8d3541-579e-47eb-8141-5da2cbb5201d · outbound

This paper cites On the Role of Computation in Reinforcement Learning.

Inverting the Bellman Equation: From $Q$-Values to World Models On the Role of Computation in Reinforcement Learning

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-07-04T06:19:38.676723Z

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Observation bb7697b3-384b-42a6-8b2e-bdc2f6c2bfc0 · outbound

This paper cites Goal-conditioned agents that learn everything all at once, 2026.

Inverting the Bellman Equation: From $Q$-Values to World Models Goal-conditioned agents that learn everything all at once, 2026

Reference 47

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Observation 03aa3f7a-70f3-43f6-a6c5-446ee40f385e · outbound

This paper cites General agents need world models.

Inverting the Bellman Equation: From $Q$-Values to World Models General agents need world models

Reference 48

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Observation 3f2272cc-03a8-4763-b713-e3073399f67d · outbound

This paper cites Inferring transition dynamics from value functions.

Inverting the Bellman Equation: From $Q$-Values to World Models Inferring transition dynamics from value functions

Reference 49

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Observation edf6ca7a-7c06-470b-8b0f-f2e696ef8ddb · outbound

This paper cites On avoiding power-seeking by artificial intelligence, 2022.

Inverting the Bellman Equation: From $Q$-Values to World Models On avoiding power-seeking by artificial intelligence, 2022

Reference 50

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Observation 1c691e96-b204-4c9a-828f-1954b197f9fb · outbound

This paper cites Interpreting emergent planning in model-free reinforcement learning.

Inverting the Bellman Equation: From $Q$-Values to World Models Interpreting emergent planning in model-free reinforcement learning

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation db1fe10e-0c78-43c2-95a9-8975ad31ef84 · outbound

This paper cites Improving generalization for temporal difference learning: The successor representation.

Inverting the Bellman Equation: From $Q$-Values to World Models Improving generalization for temporal difference learning: The successor representation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation 8547d4a8-b659-4a59-8eb7-0b6150757b04 · outbound

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Inverting the Bellman Equation: From $Q$-Values to World Models Unresolved cited work

Reference 53

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Observation 48d23b18-1ec4-4672-8fcf-c75fe2c6ea5c · outbound

This paper cites Reward-free exploration for reinforcement learning.

Inverting the Bellman Equation: From $Q$-Values to World Models Reward-free exploration for reinforcement learning

Reference 54

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Observation 23fb4771-b008-42f6-8422-07bf3715f0bd · outbound

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Inverting the Bellman Equation: From $Q$-Values to World Models Unresolved cited work

Reference 55

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Observation a235d0c0-7f0f-486b-90da-b1bf140b7611 · outbound

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Inverting the Bellman Equation: From $Q$-Values to World Models Unresolved cited work

Reference 56

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Observation f1af29db-560f-4b00-833d-c81aab01c653 · outbound

This paper cites Monte carlo gradient estimation in machine learning.J.

Inverting the Bellman Equation: From $Q$-Values to World Models Monte carlo gradient estimation in machine learning.J

Reference 57

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Observation 60b0844c-b262-412a-b3fc-74268e0b92b9 · outbound

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Inverting the Bellman Equation: From $Q$-Values to World Models Unresolved cited work

Reference 58

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Observation 536014d0-e5a8-4b3c-98d3-06e6aec049d3 · outbound

This paper cites Krantz and Harold R.

Inverting the Bellman Equation: From $Q$-Values to World Models Krantz and Harold R

Reference 59

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Observation fa9bac1e-1144-48e3-8891-e0f1a228b6f0 · outbound

This paper cites Folland.Real Analysis: Modern Techniques and Their Applications.

Inverting the Bellman Equation: From $Q$-Values to World Models Folland.Real Analysis: Modern Techniques and Their Applications

Reference 60

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation eb1d3519-ce46-4bea-a7a6-4499bd0067bf · outbound

This paper cites Lee.Introduction to Smooth Manifolds.

Inverting the Bellman Equation: From $Q$-Values to World Models Lee.Introduction to Smooth Manifolds

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation 9d51f263-7e39-4294-8499-c0c0d0ffdc76 · outbound

This paper cites University of California Press, 1955.

Inverting the Bellman Equation: From $Q$-Values to World Models University of California Press, 1955

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation 43119295-71c7-47d6-87e6-24c4f8d3f496 · outbound

This paper cites Stein and Guido Weiss.Introduction to Fourier Analysis on Euclidean Spaces.

Inverting the Bellman Equation: From $Q$-Values to World Models Stein and Guido Weiss.Introduction to Fourier Analysis on Euclidean Spaces

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation 86bd277f-29f9-4b72-b84f-bb9afce24b59 · outbound

This paper cites gymnax: A JAX-based reinforcement learning environment library, 2022.

Inverting the Bellman Equation: From $Q$-Values to World Models gymnax: A JAX-based reinforcement learning environment library, 2022

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Unavailable: canonical work link unavailable.

Observation 1d1b043c-7752-475a-a598-d4d9aa147608 · outbound

This paper cites JAX: compos- able transformations of Python+NumPy programs, 2018.

Inverting the Bellman Equation: From $Q$-Values to World Models JAX: compos- able transformations of Python+NumPy programs, 2018

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation e046c362-f326-44c0-b48c-c114b26e9dae · outbound

This paper cites test functions.

Inverting the Bellman Equation: From $Q$-Values to World Models test functions

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation 090e8102-020d-43f2-901d-9f2e2b5d5502 · outbound

This paper cites exceptionally rare.

Inverting the Bellman Equation: From $Q$-Values to World Models exceptionally rare

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation 048e04b6-a442-4525-8284-2e8a5ced459e · outbound

This paper cites an unresolved cited work.

Inverting the Bellman Equation: From $Q$-Values to World Models Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation 58ad8ed6-397f-4cc7-8ab2-3c8f10d91733 · outbound

This paper cites The chosen action is realised w.p.

Inverting the Bellman Equation: From $Q$-Values to World Models The chosen action is realised w.p

Reference 69

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation 4bedc458-1ca7-4734-a281-35d5c7077939 · outbound

This paper cites an unresolved cited work.

Inverting the Bellman Equation: From $Q$-Values to World Models Unresolved cited work

Reference 70

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation b230592c-656d-40e5-8f7a-75beafa37cf1 · outbound

This paper cites Four cells (4,4),(4,6),(6,4),(6,6) teleport the agent uniformlyinto the 16 cells of the diagonally opposite 4×4 room.

Inverting the Bellman Equation: From $Q$-Values to World Models Four cells (4,4),(4,6),(6,4),(6,6) teleport the agent uniformlyinto the 16 cells of the diagonally opposite 4×4 room

Reference 71

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Observation 81a754dd-d499-4976-93ed-3856f489cd7e · outbound

This paper cites We train PQN on |G| ∈ {1,2,3,4} training goals over 10 seeds, recover ˆP via the local LP, and evaluate on the same two unseen goals as the deterministic variant.

Inverting the Bellman Equation: From $Q$-Values to World Models We train PQN on |G| ∈ {1,2,3,4} training goals over 10 seeds, recover ˆP via the local LP, and evaluate on the same two unseen goals as the deterministic variant

Reference 72

Resolution: unresolved; no resolver link, observed 2026-06-26T14:33:45.062290Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7af62fe6-01b8-44fc-9263-582ec8dc2c53 · inbound

From Optimal Actions to World Models: Identifiability of Transition Kernels in Discounted MDPs cites this paper.

From Optimal Actions to World Models: Identifiability of Transition Kernels in Discounted MDPs Inverting the Bellman Equation: From $Q$-Values to World Models

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-10T10:53:30.692533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.