Is Exploration or Optimization the Problem for Deep Reinforcement Learning? | Paper Citation Record

Observation 1eba9a00-639c-4083-85cb-f978864228db · outbound

This paper cites Towards Characterizing Divergence in Deep Q-Learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Towards Characterizing Divergence in Deep Q-Learning

Reference 1

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Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Deep reinforcement learning at the edge of the statistical precipice

Reference 2

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This paper cites Atari-5: Distilling the Arcade Learning Environment down to Five Games.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Atari-5: Distilling the Arcade Learning Environment down to Five Games

Reference 3

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This paper cites Never give up: Learning directed exploration strategies, 2020.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Never give up: Learning directed exploration strategies, 2020

Reference 4

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This paper cites Unifying count-based exploration and intrinsic motivation.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Unifying count-based exploration and intrinsic motivation

Reference 5

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This paper cites Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling

Reference 6

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This paper cites Bellemare, Will Dabney, and R \' e mi Munos.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Bellemare, Will Dabney, and R \' e mi Munos

Reference 7

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This paper cites The theory of dynamic programming.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? The theory of dynamic programming

Reference 8

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This paper cites Interference and generalization in temporal difference learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Interference and generalization in temporal difference learning

Reference 9

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This paper cites Exploration by random network distillation, 2018 a.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Exploration by random network distillation, 2018 a

Reference 10

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This paper cites Exploration by random network distillation.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Exploration by random network distillation

Reference 11

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Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Unresolved cited work

Reference 12

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Observation 03652976-be24-49f7-bf0e-8ecf3ab81883 · outbound

This paper cites Target Network and Truncation Overcome The Deadly Triad in $Q$-Learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Target Network and Truncation Overcome The Deadly Triad in $Q$-Learning

Reference 13

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Observation b52edd0c-0814-40e9-be24-7cde3cdbe10e · outbound

This paper cites Phasic policy gradient.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Phasic policy gradient

Reference 14

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This paper cites Loss of plasticity in deep continual learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Loss of plasticity in deep continual learning

Reference 15

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This paper cites Stop regressing: Training value functions via classification for scalable deep RL.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Stop regressing: Training value functions via classification for scalable deep RL

Reference 16

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This paper cites Fujimoto, H.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Fujimoto, H

Reference 17

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Observation 40ed1020-c78a-49f5-b408-d40e0818c4a5 · outbound

This paper cites Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset

Reference 18

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This paper cites Improving performance in reinforcement learning by breaking generalization in neural networks.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Improving performance in reinforcement learning by breaking generalization in neural networks

Reference 19

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Observation 398644d4-ff48-4007-b3e2-65ca964f6c89 · outbound

This paper cites Haarnoja, A.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Haarnoja, A

Reference 20

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Observation 3c9e5f52-3bf3-4823-b7ec-01384df6b968 · outbound

This paper cites Temporal difference learning for model predictive control.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Temporal difference learning for model predictive control

Reference 21

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This paper cites Rainbow: Combining improvements in deep reinforcement learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Rainbow: Combining improvements in deep reinforcement learning

Reference 22

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This paper cites Approximately optimal approximate reinforcement learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Approximately optimal approximate reinforcement learning

Reference 23

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This paper cites Discor: Corrective feedback in reinforcement learning via distribution correction.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Discor: Corrective feedback in reinforcement learning via distribution correction

Reference 24

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Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Unresolved cited work

Reference 25

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This paper cites Maxmin q-learning: Controlling the estimation bias of q-learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Maxmin q-learning: Controlling the estimation bias of q-learning

Reference 26

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This paper cites Optidice: Offline policy optimization via stationary distribution correction estimation.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Optidice: Offline policy optimization via stationary distribution correction estimation

Reference 27

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This paper cites Hyar: Addressing discrete-continuous action reinforcement learning via hybrid action representation.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Hyar: Addressing discrete-continuous action reinforcement learning via hybrid action representation

Reference 28

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Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Unresolved cited work

Reference 29

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This paper cites Understanding plasticity in neural networks.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Understanding plasticity in neural networks

Reference 30

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Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Normalization and effective learning rates in reinforcement learning

Reference 31

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Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Human-level control through deep reinforcement learning

Reference 32

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This paper cites AlgaeDICE: Policy Gradient from Arbitrary Experience.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? AlgaeDICE: Policy Gradient from Arbitrary Experience

Reference 33

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Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 34

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This paper cites Nikishin, M.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Nikishin, M

Reference 35

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This paper cites Mixtures of Experts Unlock Parameter Scaling for Deep RL.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Mixtures of Experts Unlock Parameter Scaling for Deep RL

Reference 36

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This paper cites Chatgpt: Optimizing language models for dialogue, 2022.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Chatgpt: Optimizing language models for dialogue, 2022

Reference 37

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This paper cites Dota 2 with large scale deep reinforcement learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Dota 2 with large scale deep reinforcement learning

Reference 38

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This paper cites Bellemare, Aaron van den Oord, and Remi Munos.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Bellemare, Aaron van den Oord, and Remi Munos

Reference 39

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This paper cites The difficulty of passive learning in deep reinforcement learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? The difficulty of passive learning in deep reinforcement learning

Reference 40

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This paper cites Jha, Toshisada Mariyama, and Daniel Nikovski.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Jha, Toshisada Mariyama, and Daniel Nikovski

Reference 41

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This paper cites Fuzzy tiling activations: A simple approach to learning sparse representations online.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Fuzzy tiling activations: A simple approach to learning sparse representations online

Reference 42

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This paper cites Efros, and Trevor Darrell.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Efros, and Trevor Darrell

Reference 43

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This paper cites Bridging the gap between target networks and functional regularization.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Bridging the gap between target networks and functional regularization

Reference 44

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Observation d6832d5f-b75a-401a-b8b8-187ff913c706 · outbound

This paper cites Decoupling value and policy for generalization in reinforcement learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Decoupling value and policy for generalization in reinforcement learning

Reference 45

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Observation b7edd74f-59f8-414a-98b4-47d23bbb7de1 · outbound

This paper cites Prioritized experience replay.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Prioritized experience replay

Reference 46

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Observation 9c29176a-8c64-4fbb-8bf2-d94bd3197d30 · outbound

This paper cites Schulman, S.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Schulman, S

Reference 47

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Observation baeb9d29-7246-4a99-ba62-f1f95bb9e9ef · outbound

This paper cites Proximal Policy Optimization Algorithms.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Proximal Policy Optimization Algorithms

Reference 48

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This paper cites Courville, Marc G.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Courville, Marc G

Reference 49

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Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Unresolved cited work

Reference 50

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Observation f803f178-40de-4eb6-b7de-bdb65e7ad3b7 · outbound

This paper cites \#exploration: A study of count-based exploration for deep reinforcement learning, 2017.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? \#exploration: A study of count-based exploration for deep reinforcement learning, 2017

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:44:15.817834Z

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Observation b5bdd617-acf5-4402-9c79-250644d4e6ae · outbound

This paper cites Improving deep reinforcement learning by reducing the chain effect of value and policy churn.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Improving deep reinforcement learning by reducing the chain effect of value and policy churn

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:44:15.802566Z

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Observation b92963fa-2d7e-45e5-8748-23f0767b9695 · outbound

This paper cites Temporal difference learning and td-gammon.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Temporal difference learning and td-gammon

Reference 53

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This paper cites Deep reinforcement learning with double q-learning.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Deep reinforcement learning with double q-learning

Reference 54

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Observation 4d54ad17-5829-4f9c-a0f1-9b53f8d0d437 · outbound

This paper cites Deep Reinforcement Learning and the Deadly Triad.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Deep Reinforcement Learning and the Deadly Triad

Reference 55

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This paper cites Overcoming the spectral bias of neural value approximation.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Overcoming the spectral bias of neural value approximation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:44:15.766412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ac19e42c-0955-4e59-9db1-4a4516f45894 · outbound

This paper cites MinAtar : An atari-inspired testbed for thorough and reproducible reinforcement learning experiments.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? MinAtar : An atari-inspired testbed for thorough and reproducible reinforcement learning experiments

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:44:15.749648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 60d6ec9f-97b9-48af-8fe1-a0a208a49cc9 · outbound

This paper cites Learning invariant representations for reinforcement learning without reconstruction.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Learning invariant representations for reinforcement learning without reconstruction

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:44:15.734634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fdbd5858-aa05-4113-a705-2a16f45e2761 · outbound

This paper cites Gendice: Generalized offline estimation of stationary values.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Gendice: Generalized offline estimation of stationary values

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:44:15.719434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 719dedf7-6b7d-4556-ad52-758912305210 · outbound

This paper cites Breaking the deadly triad with a target network.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Breaking the deadly triad with a target network

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:44:15.704127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 76405fa5-0000-4990-93c4-51b2eff046fd · outbound

This paper cites BeBold: Exploration Beyond the Boundary of Explored Regions.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? BeBold: Exploration Beyond the Boundary of Explored Regions

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T05:44:15.267730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4bfed1c-e7c4-4ae2-8c29-abd91c3ee521 · outbound

This paper cites Noveld: A simple yet effective exploration criterion.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Noveld: A simple yet effective exploration criterion

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:44:15.689132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3ac185bc-b5ee-42b2-a8b6-661b1dc03eea · outbound

This paper cites @esa (Ref.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? @esa (Ref

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T05:44:15.279905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2de190b9-18af-4c9c-a78a-2fde7f7fe7d6 · outbound

This paper cites an unresolved cited work.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Unresolved cited work

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T05:44:15.285200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b258ad46-e590-4175-8533-77a1ac54c371 · outbound

This paper cites an unresolved cited work.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-06T05:44:15.653607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers