Paper Citation Record · LEDGER

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models

As of 21 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2605.14897.

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

pith.paper-citation-record.v1
2605.14897 v1

Coverage vector

measured 35 of 35 reference resolution

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measured 35 of 35 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

35 of 35 outbound references displayed

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External citation measurements

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Outbound references

Observation 6e64ed2e-0f41-42e3-8073-ca3d6365e2c6 · outbound

This paper cites Human-level control through deep reinforcement learning.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Human-level control through deep reinforcement learning

Reference 1

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This paper cites Continuous control with deep reinforcement learning.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Continuous control with deep reinforcement learning

Reference 2

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Observation fd7c5384-8ee9-449c-b61e-e89f5628d97f · outbound

This paper cites A survey of reinforcement learning algorithms for dynamically varying environments.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models A survey of reinforcement learning algorithms for dynamically varying environments

Reference 3

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This paper cites A Tour of Reinforcement Learning: The View from Con- tinuous Control.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models A Tour of Reinforcement Learning: The View from Con- tinuous Control

Reference 4

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Observation 9e6d0d96-8af3-46d4-a1bf-57b7308bed22 · outbound

This paper cites Artiﬁcial Intelligence 267, pp.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Artiﬁcial Intelligence 267, pp

Reference 5

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Observation a7177cec-83ea-4666-8bb6-f6f3cf805d72 · outbound

This paper cites Generating Inter- pretable Fuzzy Controllers Using Particle Swarm Optimization and Ge- neticProgramming.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Generating Inter- pretable Fuzzy Controllers Using Particle Swarm Optimization and Ge- neticProgramming

Reference 6

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Observation cff4da1d-d48a-4377-95d6-8b30bb629495 · outbound

This paper cites Imitation-Projected Programmatic Reinforcement Learning.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Imitation-Projected Programmatic Reinforcement Learning

Reference 7

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Observation 614a61ef-4fc5-42bf-906c-4e5303ccdcb7 · outbound

This paper cites Explainable Reinforcement Learning (XRL): A Sys- tematic Literature Review and Taxonomy.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Explainable Reinforcement Learning (XRL): A Sys- tematic Literature Review and Taxonomy

Reference 8

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This paper cites Distilling a Neural Network Into a Soft Decision Tree.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Distilling a Neural Network Into a Soft Decision Tree

Reference 9

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Observation 6f3798ca-4b3a-4088-a20b-b429d944530e · outbound

This paper cites Policy Distillation.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Policy Distillation

Reference 10

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Observation ca9714d0-010e-4647-980f-05dd55e77ea2 · outbound

This paper cites Sutton and Andrew Barto.Reinforcement Learning: An Intro- duction.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Sutton and Andrew Barto.Reinforcement Learning: An Intro- duction

Reference 11

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This paper cites A Markovian Decision Process.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models A Markovian Decision Process

Reference 12

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This paper cites A Natural Policy Gradient.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models A Natural Policy Gradient

Reference 13

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This paper cites Q-Learning.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Q-Learning

Reference 14

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This paper cites Actor-Critic Algorithms.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Actor-Critic Algorithms

Reference 15

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Observation 88243f76-f562-4288-8cad-b3a81492377c · outbound

This paper cites Soft Actor-Critic: Off-policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Soft Actor-Critic: Off-policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Reference 16

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Observation 6f5e2366-bcd3-42d4-84e8-fa41ab79cd2a · outbound

This paper cites Proximal Policy Optimization Algorithms.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Proximal Policy Optimization Algorithms

Reference 17

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This paper cites Louis, G.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Louis, G

Reference 18

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This paper cites Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents

Reference 19

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This paper cites Explaining Deep Adaptive Programs via Reward De- composition.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Explaining Deep Adaptive Programs via Reward De- composition

Reference 20

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Observation c48c7fd0-d3a1-4733-b378-75b20ac5e0c6 · outbound

This paper cites Toward a Psychology of Deep Reinforce- ment Learning Agents Using a Cognitive Architecture.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Toward a Psychology of Deep Reinforce- ment Learning Agents Using a Cognitive Architecture

Reference 21

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Observation 6162c8d2-f745-4d88-884f-6d001cbc22ff · outbound

This paper cites Understanding Individual Agent Importance in Multi-Agent System via Counterfactual Reasoning.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Understanding Individual Agent Importance in Multi-Agent System via Counterfactual Reasoning

Reference 22

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This paper cites Vector Quantization.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Vector Quantization

Reference 23

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This paper cites doi: 10.1145/361002.361007.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models doi: 10.1145/361002.361007

Reference 24

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This paper cites Some Methods for Classification and Analysis of Multi- Variate Observations.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Some Methods for Classification and Analysis of Multi- Variate Observations

Reference 25

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This paper cites Why Should I Trust You?.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Why Should I Trust You?

Reference 26

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This paper cites Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

Reference 27

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Observation ec600faf-82a8-4dbb-8b83-11f56c21a2d5 · outbound

This paper cites Explaining a Deep Reinforcement Learning Dock- ing Agent Using Linear Model Trees with User Adapted Visualization.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Explaining a Deep Reinforcement Learning Dock- ing Agent Using Linear Model Trees with User Adapted Visualization

Reference 28

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This paper cites Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

Reference 29

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This paper cites C., Grantcharov, V ., Wanna, S., & others.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models C., Grantcharov, V ., Wanna, S., & others

Reference 30

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This paper cites Interpretable and Editable Programmatic Tree Poli- cies for Reinforcement Learning.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Interpretable and Editable Programmatic Tree Poli- cies for Reinforcement Learning

Reference 31

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This paper cites Distilling Deep Reinforcement Learning Policies in Soft Decision Trees.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Distilling Deep Reinforcement Learning Policies in Soft Decision Trees

Reference 32

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Observation 8244284e-4ac7-4350-a0f1-b9d1e901ef1c · outbound

This paper cites Adaptive State Space Partitioning for Reinforcement Learning.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Adaptive State Space Partitioning for Reinforcement Learning

Reference 33

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This paper cites Li, 3d fully convolutional network for vehicle de- tection in point cloud, in: 2017 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS), 2017, pp.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Li, 3d fully convolutional network for vehicle de- tection in point cloud, in: 2017 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS), 2017, pp

Reference 34

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This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 35

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