Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network | Paper Citation Record

Observation 91374a6e-fc4f-4e94-bfff-27c6b7243ffc · outbound

This paper cites Layer Normalization.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Layer Normalization

Reference 1

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This paper cites J., Smith, L., Kostrikov, I., and Levine, S.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network J., Smith, L., Kostrikov, I., and Levine, S

Reference 2

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This paper cites Dota 2 with Large Scale Deep Reinforcement Learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Dota 2 with Large Scale Deep Reinforcement Learning

Reference 3

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This paper cites Offline rl without off-policy evaluation.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Offline rl without off-policy evaluation

Reference 4

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Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Unresolved cited work

Reference 5

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This paper cites A., Salazar, G., Tran, H.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network A., Salazar, G., Tran, H

Reference 6

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This paper cites Decision transformer: Reinforcement learning via sequence modeling.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Decision transformer: Reinforcement learning via sequence modeling

Reference 7

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This paper cites Rvs: What is essential for offline RL via supervised learning? In International Conference on Learning Representations, 2022.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Rvs: What is essential for offline RL via supervised learning? In International Conference on Learning Representations, 2022

Reference 8

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Observation 1fecd2b1-1114-40f1-a0e2-6ef574376969 · outbound

This paper cites Counterfactual multi-agent policy gradients.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Counterfactual multi-agent policy gradients

Reference 9

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Observation db3b8a5c-55dd-40ad-a00f-948b388fe3ef · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 10

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Observation 7cf1abf2-b44b-437b-90f8-0fe4bb516db4 · outbound

This paper cites and Gu, S.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network and Gu, S

Reference 11

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Observation 2e974eba-5512-4ef2-9190-856862d12ee9 · outbound

This paper cites Addressing function approximation error in actor-critic methods.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Addressing function approximation error in actor-critic methods

Reference 12

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Observation 26ad1416-bb4e-4828-8072-f452aba5f1ce · outbound

This paper cites Reinforcement learning with deep energy-based policies.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Reinforcement learning with deep energy-based policies

Reference 13

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Observation 2e7cc4ae-b972-48a4-be71-8365288c2187 · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 14

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Observation 762c91c2-2744-4237-b726-5ae2ce80c82c · outbound

This paper cites Modem: Accelerating visual model-based reinforcement learning with demonstrations.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Modem: Accelerating visual model-based reinforcement learning with demonstrations

Reference 15

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Observation bd92b01e-1c3a-4daf-b9b1-0e7201050ce0 · outbound

This paper cites Neural networks: a comprehensive foundation.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Neural networks: a comprehensive foundation

Reference 16

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Observation 12eb4e6e-2e22-44b3-ad2c-b7be9411f06e · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Gaussian Error Linear Units (GELUs)

Reference 17

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This paper cites Deep q-learning from demonstrations.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Deep q-learning from demonstrations

Reference 18

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Observation 6c80aa3c-ffd2-4295-be7a-5301295dc6a5 · outbound

This paper cites B ayesian design principles for offline-to-online reinforcement learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network B ayesian design principles for offline-to-online reinforcement learning

Reference 19

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Observation 493f95b0-9b97-4569-bfea-0995ca08aa92 · outbound

This paper cites R., and Davison, A.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network R., and Davison, A

Reference 20

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Observation b47e30cf-10fb-42c2-b51d-1a33e754ab05 · outbound

This paper cites Offline reinforcement learning with implicit q-learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Offline reinforcement learning with implicit q-learning

Reference 21

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Observation ccf644af-9f28-4a5f-b182-1954113cb865 · outbound

This paper cites Conservative q-learning for offline reinforcement learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Conservative q-learning for offline reinforcement learning

Reference 22

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This paper cites Offline-to-online reinforcement learning via balanced replay and pessimistic q-ensemble.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Offline-to-online reinforcement learning via balanced replay and pessimistic q-ensemble

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:39:59.837241Z

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Observation aaf53e1a-99d2-490a-aa35-c56a737c90fb · outbound

This paper cites Uni-o4: Unifying online and offline deep reinforcement learning with multi-step on-policy optimization.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Uni-o4: Unifying online and offline deep reinforcement learning with multi-step on-policy optimization

Reference 24

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This paper cites A survey of convolutional neural networks: Analysis, applications, and prospects.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network A survey of convolutional neural networks: Analysis, applications, and prospects

Reference 25

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This paper cites Continuous control with deep reinforcement learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Continuous control with deep reinforcement learning

Reference 26

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Observation 824cb68b-7c81-4977-99f0-f85c9e5c024a · outbound

This paper cites Discrete Sequential Prediction of Continuous Actions for Deep RL.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Discrete Sequential Prediction of Continuous Actions for Deep RL

Reference 27

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Observation d4425530-19bc-4373-a556-f48c66a2efe0 · outbound

This paper cites A., Veness, J., Bellemare, M.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network A., Veness, J., Bellemare, M

Reference 28

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Observation 02cadaba-4b3f-4c95-bc9b-b71a628de3d4 · outbound

This paper cites Overcoming exploration in reinforcement learning with demonstrations.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Overcoming exploration in reinforcement learning with demonstrations

Reference 29

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Observation f3d12f85-0098-4afe-b502-e03eb612d060 · outbound

This paper cites Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning

Reference 30

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This paper cites Learning complex dexterous manipulation with deep reinforcement learning and demonstrations, 2018.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Learning complex dexterous manipulation with deep reinforcement learning and demonstrations, 2018

Reference 31

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Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Unresolved cited work

Reference 32

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This paper cites Mastering atari, go, chess and shogi by planning with a learned model.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Mastering atari, go, chess and shogi by planning with a learned model

Reference 33

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This paper cites Proximal Policy Optimization Algorithms.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Proximal Policy Optimization Algorithms

Reference 34

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This paper cites and Abbeel, P.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network and Abbeel, P

Reference 35

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This paper cites Continuous control with coarse-to-fine reinforcement learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Continuous control with coarse-to-fine reinforcement learning

Reference 36

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This paper cites Solving continuous control via q-learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Solving continuous control via q-learning

Reference 37

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Observation 04f49af1-4824-4fed-b2dd-03e662db1c07 · outbound

This paper cites Growing Q -networks: S olving continuous control tasks with adaptive control resolution.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Growing Q -networks: S olving continuous control tasks with adaptive control resolution

Reference 38

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This paper cites Mastering the game of go without human knowledge.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Mastering the game of go without human knowledge

Reference 39

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This paper cites and Agrawal, S.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network and Agrawal, S

Reference 40

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This paper cites Action branching architectures for deep reinforcement learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Action branching architectures for deep reinforcement learning

Reference 41

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This paper cites Learning to represent action values as a hypergraph on the action vertices.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Learning to represent action values as a hypergraph on the action vertices

Reference 42

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Observation c3230589-da4a-48a3-a5e1-f6b7a96708c4 · outbound

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

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Deep reinforcement learning with double q-learning

Reference 43

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This paper cites Discriminator-weighted offline imitation learning from suboptimal demonstrations.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Discriminator-weighted offline imitation learning from suboptimal demonstrations

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:39:59.719141Z

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Observation f2673bbe-0e28-409c-acef-3c4fb7a9d297 · outbound

This paper cites Hd-cnn: Hierarchical deep convolutional neural networks for large scale visual recognition.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Hd-cnn: Hierarchical deep convolutional neural networks for large scale visual recognition

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:39:59.708249Z

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Observation b7835a39-4772-4a97-9a2c-832d712639db · outbound

This paper cites Mastering visual continuous control: Improved data-augmented reinforcement learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Mastering visual continuous control: Improved data-augmented reinforcement learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:39:59.695917Z

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Observation 51b5b9c5-0637-44b1-8770-9a8c6aa53510 · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network The surprising effectiveness of ppo in cooperative multi-agent games

Reference 47

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Observation 0094e1ab-6b8c-48c7-92db-8cf0d2792c8b · outbound

This paper cites Policy expansion for bridging offline-to-online reinforcement learning.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Policy expansion for bridging offline-to-online reinforcement learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:39:59.674731Z

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This paper cites Z., Kumar, V., Levine, S., and Finn, C.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network Z., Kumar, V., Levine, S., and Finn, C

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:39:59.664137Z

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Observation c7add5a0-d9ee-428d-8d03-a620df032e05 · outbound

This paper cites D., Maas, A., Bagnell, J.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network D., Maas, A., Bagnell, J

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:39:59.653624Z

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Observation 4a2aa577-c749-4a7a-bd3d-e5e44f236f26 · outbound

This paper cites write newline.

Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network write newline

Reference 51

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