Paper Citation Record · LEDGER

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation

As of 12 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2411.16532.

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

pith.paper-citation-record.v1
2411.16532 v1

Coverage vector

measured 48 of 48 reference resolution

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measured 48 of 48 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 3d6f0111-0dc4-4dad-ac00-972585748772 · outbound

This paper cites Cells 10(4), 735 (2021).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Cells 10(4), 735 (2021)

Reference 1

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Unresolved cited work

Reference 2

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This paper cites Science 245(4918), 605–615 (1989) 30.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Science 245(4918), 605–615 (1989) 30

Reference 3

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This paper cites Neural networks 113, 54–71 (2019).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Neural networks 113, 54–71 (2019)

Reference 4

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This paper cites Journal of Artificial Intelligence Research 61, 523–562 (2018).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Journal of Artificial Intelligence Research 61, 523–562 (2018)

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This paper cites To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

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This paper cites Curiosity-driven Exploration by Self-supervised Prediction.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Curiosity-driven Exploration by Self-supervised Prediction

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This paper cites Advances in Neural Information Processing Systems 34, 20516–20530 (2021).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Advances in Neural Information Processing Systems 34, 20516–20530 (2021)

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This paper cites Advances in neural information processing systems 12 (1999).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Advances in neural information processing systems 12 (1999)

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This paper cites Advances in Neural Information Processing Systems 33, 11734–11743 (2020).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Advances in Neural Information Processing Systems 33, 11734–11743 (2020)

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Observation e668401b-a5e1-4d5d-b45c-9c77a738700e · outbound

This paper cites Proceedings of the national academy of sciences 114(13), 3521–3526 (2017).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Proceedings of the national academy of sciences 114(13), 3521–3526 (2017)

Reference 11

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Observation f9854f95-356f-43d3-85b2-5734f2bd88bf · outbound

This paper cites Progressive Neural Networks.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Progressive Neural Networks

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This paper cites In: International Conference on Machine Learning, pp.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: International Conference on Machine Learning, pp

Reference 13

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This paper cites Task Agnostic Continual Learning Using Online Variational Bayes.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Task Agnostic Continual Learning Using Online Variational Bayes

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This paper cites In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 31 pp.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 31 pp

Reference 15

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This paper cites The Arcade Learning Environment: An Evaluation Platform for General Agents.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation The Arcade Learning Environment: An Evaluation Platform for General Agents

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This paper cites In: ICLR (2016).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: ICLR (2016)

Reference 17

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Observation c5d5095a-d4ef-413f-b0f0-087cb09207d1 · outbound

This paper cites In: The 22nd International Conference on Artificial Intelligence and Statistics, pp.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: The 22nd International Conference on Artificial Intelligence and Statistics, pp

Reference 18

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Observation df9801a2-ea65-4026-ad22-c81c6ac4f70b · outbound

This paper cites Advances in Neural Information Processing Systems 34, 6920–6933 (2021).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Advances in Neural Information Processing Systems 34, 6920–6933 (2021)

Reference 19

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This paper cites Real-time Policy Distillation in Deep Reinforcement Learning.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Real-time Policy Distillation in Deep Reinforcement Learning

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This paper cites In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp

Reference 21

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This paper cites In: 2017 International Joint Conference on Neural Networks (IJCNN), pp.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: 2017 International Joint Conference on Neural Networks (IJCNN), pp

Reference 22

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This paper cites IEEE Transactions on Cognitive and Developmental Systems (2023).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation IEEE Transactions on Cognitive and Developmental Systems (2023)

Reference 23

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This paper cites In: International Conference on Learning Representations (2018).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: International Conference on Learning Representations (2018)

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This paper cites Nature communications 11(1), 4069 (2020).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Nature communications 11(1), 4069 (2020)

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This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

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This paper cites Advances in neural information processing 32 systems 32 (2019).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Advances in neural information processing 32 systems 32 (2019)

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Representational Continuity for Unsupervised Continual Learning

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This paper cites Journal of Neuroscience 35(3), 1319–1334 (2015).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Journal of Neuroscience 35(3), 1319–1334 (2015)

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Distilling the Knowledge in a Neural Network

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Advances in neural information processing systems 17 (2004)

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Neuron 36(2), 285–298 (2002)

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This paper cites In: 2017 Joint IEEE Inter- national Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: 2017 Joint IEEE Inter- national Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: Proc

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Playing Atari with Deep Reinforcement Learning

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Deep Reinforcement Learning: An Overview

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: International Conference on Machine Learning, pp

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This paper cites Advances in neural information processing systems 30 (2017).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Advances in neural information processing systems 30 (2017)

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This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation High-Dimensional Continuous Control Using Generalized Advantage Estimation

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation A Comprehensive Survey of Continual Learning: Theory, Method and Application

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation The Royal Society (2017)

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This paper cites Three scenarios for continual learning.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Three scenarios for continual learning

Reference 42

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation In: 2020 IEEE Conference on Games (CoG), pp

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This paper cites Advances in neural information processing systems 32 (2019).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Advances in neural information processing systems 32 (2019)

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Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation GitHub (2018)

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This paper cites Journal of Machine Learning Research 22(268), 1–8 (2021).

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Journal of Machine Learning Research 22(268), 1–8 (2021)

Reference 46

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This paper cites Avalanche RL: a Continual Reinforcement Learning Library.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Avalanche RL: a Continual Reinforcement Learning Library

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This paper cites IEEE transactions on pattern analysis and machine intelligence 44(10), 6715–6728 (2021) 34.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation IEEE transactions on pattern analysis and machine intelligence 44(10), 6715–6728 (2021) 34

Reference 48

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Pith citing papers

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