Paper Citation Record · LEDGER

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values

As of 18 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2505.07797.

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

pith.paper-citation-record.v1
2505.07797 v2

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

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measured 68 of 68 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

67 of 67 outbound references displayed

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External citation measurements

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Outbound references

Observation b75df8c8-fe44-4e9a-bf0a-6459514df13f · outbound

This paper cites Explaining individual predictions when features are dependent: More accurate approximations to S hapley values.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explaining individual predictions when features are dependent: More accurate approximations to S hapley values

Reference 1

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This paper cites Agent strategy summarization.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Agent strategy summarization

Reference 2

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This paper cites Weighted voting doesn't work: A mathematical analysis.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Weighted voting doesn't work: A mathematical analysis

Reference 3

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This paper cites Verifiable reinforcement learning via policy extraction.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Verifiable reinforcement learning via policy extraction

Reference 4

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Observation 65dd7d5f-9838-405f-a7ef-7ac20624e9fe · outbound

This paper cites Explaining reinforcement learning with S hapley values.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explaining reinforcement learning with S hapley values

Reference 5

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This paper cites Autonomous navigation of stratospheric balloons using reinforcement learning.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Autonomous navigation of stratospheric balloons using reinforcement learning

Reference 6

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This paper cites Tripletree: A versatile interpretable representation of black box agents and their environments.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Tripletree: A versatile interpretable representation of black box agents and their environments

Reference 7

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This paper cites O pen AI Gym , 2016.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values O pen AI Gym , 2016

Reference 8

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This paper cites Explainable AI for path following with model trees.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explainable AI for path following with model trees

Reference 9

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Observation ba79d6c8-171b-48a8-9a3d-2205dc1cb807 · outbound

This paper cites Understanding global feature contributions with additive importance measures.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Understanding global feature contributions with additive importance measures

Reference 10

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Observation f36a8e2f-40c3-48cf-bb5c-2d8012223a94 · outbound

This paper cites Explaining by removing: A unified framework for model explanation.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explaining by removing: A unified framework for model explanation

Reference 11

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This paper cites Memory-based explainable reinforcement learning.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Memory-based explainable reinforcement learning

Reference 12

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This paper cites Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario

Reference 13

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This paper cites Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems

Reference 14

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Observation a8f0c82c-7a81-412b-a0ff-81b366dccd54 · outbound

This paper cites Magnetic control of tokamak plasmas through deep reinforcement learning.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Magnetic control of tokamak plasmas through deep reinforcement learning

Reference 15

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This paper cites Hierarchical reinforcement learning with the MAXQ value function decomposition.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Hierarchical reinforcement learning with the MAXQ value function decomposition

Reference 16

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This paper cites no clear winner.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values no clear winner

Reference 17

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Observation fd872556-dd6d-4845-a926-a82d6daf2466 · outbound

This paper cites Shapley explainability on the data manifold.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Shapley explainability on the data manifold

Reference 18

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Observation 338a986c-d360-4063-a34b-0b1df0033c9b · outbound

This paper cites Asymmetric S hapley values: incorporating causal knowledge into model-agnostic explainability.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Asymmetric S hapley values: incorporating causal knowledge into model-agnostic explainability

Reference 19

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This paper cites Visualizing and understanding A tari agents.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Visualizing and understanding A tari agents

Reference 20

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This paper cites Explainable deep reinforcement learning for UAV autonomous path planning.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explainable deep reinforcement learning for UAV autonomous path planning

Reference 21

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This paper cites Interpretable policies for reinforcement learning by genetic programming.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Interpretable policies for reinforcement learning by genetic programming

Reference 22

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This paper cites Establishing appropriate trust via critical states.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Establishing appropriate trust via critical states

Reference 23

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This paper cites Feature relevance quantification in explainable AI : A causal problem.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Feature relevance quantification in explainable AI : A causal problem

Reference 24

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This paper cites Fast SHAP : Real-time S hapley value estimation.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Fast SHAP : Real-time S hapley value estimation

Reference 25

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This paper cites Policy extraction via online Q -value distillation.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Policy extraction via online Q -value distillation

Reference 26

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This paper cites Explainable reinforcement learning via reward decomposition.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explainable reinforcement learning via reward decomposition

Reference 27

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Discovering symbolic policies with deep reinforcement learning

Reference 28

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explainable reinforcement learning for longitudinal control

Reference 29

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This paper cites Analysis of regression in game theory approach.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Analysis of regression in game theory approach

Reference 30

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explainable AI methods on a deep reinforcement learning agent for automatic docking

Reference 31

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values A unified approach to interpreting model predictions

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values From local explanations to global understanding with explainable AI for trees

Reference 33

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This paper cites Explainable reinforcement learning through a causal lens.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explainable reinforcement learning through a causal lens

Reference 34

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Human-level control through deep reinforcement learning

Reference 35

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This paper cites Sobol' indices and S hapley value.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Sobol' indices and S hapley value

Reference 36

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Unresolved cited work

Reference 37

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This paper cites Explain your move: Understanding agent actions using specific and relevant feature attribution.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explain your move: Understanding agent actions using specific and relevant feature attribution

Reference 38

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Causal versus marginal Shapley values for robotic lever manipulation controlled using deep reinforcement learning

Reference 39

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Reinforcement learning with explainability for traffic signal control

Reference 40

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Finding and visualizing weaknesses of deep reinforcement learning agents

Reference 41

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Towards explainable deep reinforcement learning for traffic signal control

Reference 42

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Mastering A tari, G o, chess and shogi by planning with a learned model

Reference 43

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Avoiding fusion plasma tearing instability with deep reinforcement learning

Reference 44

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values A value for n-person games

Reference 45

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Optimization methods for interpretable differentiable decision trees applied to reinforcement learning

Reference 46

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Mastering the game of G o with deep neural networks and tree search

Reference 47

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Mastering the game of G o without human knowledge

Reference 48

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This paper cites An efficient explanation of individual classifications using game theory.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values An efficient explanation of individual classifications using game theory

Reference 49

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values A general method for visualizing and explaining black-box regression models

Reference 50

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This paper cites Explaining prediction models and individual predictions with feature contributions.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explaining prediction models and individual predictions with feature contributions

Reference 51

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A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explaining instance classifications with interactions of subsets of feature values

Reference 52

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Observation a3001a63-afb2-4afc-8fce-0e4201844eb4 · outbound

This paper cites The many S hapley values for model explanation.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values The many S hapley values for model explanation

Reference 53

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This paper cites Axiomatic attribution for deep networks.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Axiomatic attribution for deep networks

Reference 54

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This paper cites Reinforcement learning: An introduction.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Reinforcement learning: An introduction

Reference 55

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This paper cites Explainable deep reinforcement learning for production control.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explainable deep reinforcement learning for production control

Reference 56

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This paper cites Iterative bounding MDP s: Learning interpretable policies via non-interpretable methods.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Iterative bounding MDP s: Learning interpretable policies via non-interpretable methods

Reference 57

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This paper cites Grandmaster level in StarCraft II using multi-agent reinforcement learning.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Grandmaster level in StarCraft II using multi-agent reinforcement learning

Reference 58

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This paper cites von Neumann and O.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values von Neumann and O

Reference 59

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This paper cites Training characteristic functions with reinforcement learning: XAI -methods play connect four.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Training characteristic functions with reinforcement learning: XAI -methods play connect four

Reference 60

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This paper cites Attribution-based salience method towards interpretable reinforcement learning.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Attribution-based salience method towards interpretable reinforcement learning

Reference 61

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This paper cites Dueling network architectures for deep reinforcement learning.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Dueling network architectures for deep reinforcement learning

Reference 62

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This paper cites Q-learning.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Q-learning

Reference 63

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This paper cites Efficient nonparametric statistical inference on population feature importance using S hapley values.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Efficient nonparametric statistical inference on population feature importance using S hapley values

Reference 64

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This paper cites Outracing champion G ran T urismo drivers with deep reinforcement learning.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Outracing champion G ran T urismo drivers with deep reinforcement learning

Reference 65

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This paper cites Explainable AI in deep reinforcement learning models: A SHAP method applied in power system emergency control.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explainable AI in deep reinforcement learning models: A SHAP method applied in power system emergency control

Reference 66

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This paper cites Explainable AI in deep reinforcement learning models for power system emergency control.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values Explainable AI in deep reinforcement learning models for power system emergency control

Reference 67

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Pith citing papers

Observation 8fafc73c-94ae-4d1c-83ea-ff5f1f9de63f · inbound

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments cites this paper.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values

Reference 7

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