ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning | Paper Citation Record

Observation 235c60b1-7a27-4f37-8fc6-4d38d2e17484 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Mastering the game of go with deep neural networks and tree search,

Reference 1

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Observation dfdd9d15-9f82-4c2b-94ff-4d7718ed6eb8 · outbound

This paper cites Self-paced prioritized curriculum learning with coverage penalty in deep reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Self-paced prioritized curriculum learning with coverage penalty in deep reinforcement learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.257982Z

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Observation 8de036a6-6b9b-4951-a8a3-2a3e6f29b9af · outbound

This paper cites Feature control as intrinsic motivation for hierarchical reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Feature control as intrinsic motivation for hierarchical reinforcement learning,

Reference 3

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Observation db12288f-8afd-429d-9e91-884444a7d45d · outbound

This paper cites Scalable deep reinforcement learning for vision-based robotic manipulation,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Scalable deep reinforcement learning for vision-based robotic manipulation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.224155Z

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Observation b25f2042-8169-445b-b231-8f4a454f94e2 · outbound

This paper cites Solving Rubik's Cube with a Robot Hand.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Solving Rubik's Cube with a Robot Hand

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.473307Z

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Observation f805c780-0b8c-47cf-a48b-2ff5bec9cc7b · outbound

This paper cites Rein- forcement learning for mobile robotics exploration: A survey,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Rein- forcement learning for mobile robotics exploration: A survey,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.208222Z

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Observation e6f386dc-8bdf-4301-9d06-fd6e6567aede · outbound

This paper cites Deductive reinforcement learning for visual autonomous urban driving navigation,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Deductive reinforcement learning for visual autonomous urban driving navigation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.194144Z

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Observation 8d2ef1ca-1939-4332-88eb-f6b11351c05b · outbound

This paper cites Deep reinforcement learning on autonomous driving policy with auxiliary critic network,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Deep reinforcement learning on autonomous driving policy with auxiliary critic network,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.183322Z

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Observation 20402e7a-53b9-4296-a045-9ee6c4d676d5 · outbound

This paper cites Cadre: A cascade deep reinforcement learning framework for vision- based autonomous urban driving,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Cadre: A cascade deep reinforcement learning framework for vision- based autonomous urban driving,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.172260Z

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Observation a1e08a39-8a21-4104-9a4b-43121234d178 · outbound

This paper cites Batch reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Batch reinforcement learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.161216Z

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Observation 1adb127c-c337-449e-812f-6b58f4173d7c · outbound

This paper cites A survey on offline reinforcement learning: Taxonomy, review, and open problems,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning A survey on offline reinforcement learning: Taxonomy, review, and open problems,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.503745Z

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Observation 8be1ed71-3d5b-4728-b4a6-9485f0892005 · outbound

This paper cites Stabilizing off- policy q-learning via bootstrapping error reduction,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Stabilizing off- policy q-learning via bootstrapping error reduction,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.137841Z

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Observation fa8d2e8c-1b1c-4c0a-b4ea-7dd6532d82d7 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.517050Z

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Observation f6d746ea-79e9-4501-93d9-5ce43ea6969c · outbound

This paper cites Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.523584Z

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Observation 06fbc69f-efd9-4eb2-a949-6b06735632aa · outbound

This paper cites Behavior Regularized Offline Reinforcement Learning.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Behavior Regularized Offline Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.528082Z

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Observation e247f579-0bd7-47c5-a7de-1c4229d8c984 · outbound

This paper cites Keep doing what worked: Behavior modelling priors for offline reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Keep doing what worked: Behavior modelling priors for offline reinforcement learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.125435Z

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Observation 1736ba04-7f8e-47c0-9f6f-4e18e973f7d2 · outbound

This paper cites Off-policy deep reinforcement learning without exploration,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Off-policy deep reinforcement learning without exploration,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.113347Z

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Observation 50fa9343-4f2b-44a6-af1e-b442eba14149 · outbound

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

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Conservative q-learning for offline reinforcement learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.099058Z

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Observation 0713a2ee-4465-427a-ba16-ce41f2ed975c · outbound

This paper cites Conservative data sharing for multi-task offline reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Conservative data sharing for multi-task offline reinforcement learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.086771Z

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Observation ce89ff47-312d-40e7-9562-e07c6ce222f8 · outbound

This paper cites Combo: Conservative offline model-based policy optimization,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Combo: Conservative offline model-based policy optimization,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.075723Z

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Observation da643c97-f944-47d4-8105-c8d8875427c6 · outbound

This paper cites Conservative offline distribu- tional reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Conservative offline distribu- tional reinforcement learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.064520Z

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Observation 555d7b47-69cf-4109-be6e-dca669343f9a · outbound

This paper cites Bail: Best-action imitation learning for batch deep reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Bail: Best-action imitation learning for batch deep reinforcement learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.052695Z

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Observation 14585ab1-c475-483e-931a-64a09915e53e · outbound

This paper cites Critic regularized regression,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Critic regularized regression,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.037377Z

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Observation 193e2922-9f58-4333-aaa8-da27096fd3ab · outbound

This paper cites Curriculum offline imitating learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Curriculum offline imitating learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.018734Z

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Observation 22140e3c-7dea-4bcf-8f46-1bdc409b13b3 · outbound

This paper cites Issues in using function approximation for reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Issues in using function approximation for reinforcement learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:43.004471Z

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Observation 7ed2cccc-ff65-4a39-80d1-69ec5764eeeb · outbound

This paper cites Diagnosing bottlenecks in deep q-learning algorithms,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Diagnosing bottlenecks in deep q-learning algorithms,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.993397Z

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Observation aed201d5-eb30-42c8-ab43-f9147d08caa5 · outbound

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

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Towards Characterizing Divergence in Deep Q-Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.585742Z

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Observation 69bd9f7f-9cb8-4de0-8067-938cafc6b7dd · outbound

This paper cites Non-delusional q-learning and value-iteration,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Non-delusional q-learning and value-iteration,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.983463Z

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Observation c8739e7e-8318-4c34-b86b-353a031027c3 · outbound

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

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.972957Z

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Observation 705e8487-1669-40e4-b479-e29462368695 · outbound

This paper cites Addressing function approxi- mation error in actor-critic methods,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Addressing function approxi- mation error in actor-critic methods,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.961641Z

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Observation 11afa7e7-dcf7-44fa-87e2-30edaa3d3367 · outbound

This paper cites Adaptive qq -learning for data-based optimal output regulation with experience replay,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Adaptive qq -learning for data-based optimal output regulation with experience replay,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.949657Z

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Observation 0cfa420d-355f-4539-ab8e-79f398d2b85a · outbound

This paper cites Prioritized Experience Replay.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Prioritized Experience Replay

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.615061Z

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Observation 2f67bb5a-7b9f-48cc-9fba-427a0aee93cd · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.621882Z

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Observation f36895de-dd90-429f-af4f-1077efd51bfa · outbound

This paper cites Distributed prioritized experience replay,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Distributed prioritized experience replay,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.932546Z

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Observation f1f36a59-4a94-4869-b409-6b0b609831ef · outbound

This paper cites An equivalence between loss functions and non-uniform sampling in experience replay,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning An equivalence between loss functions and non-uniform sampling in experience replay,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.917967Z

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Observation 5cbefa5a-8d8b-4a18-97ed-c8c9b64899f7 · outbound

This paper cites Model-augmented prioritized experience replay,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Model-augmented prioritized experience replay,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.902889Z

Source-reported events for the cited work

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Observation 9612b52f-54ac-4139-a02c-3e4e877d650a · outbound

This paper cites Demystifying Reinforcement Learning in Time-Varying Systems.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Demystifying Reinforcement Learning in Time-Varying Systems

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-11T10:20:42.107881Z

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Observation 672dc61d-8d84-43b6-8e05-3cf3bc4f5b1a · outbound

This paper cites Towards continual reinforcement learning: A review and perspectives,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Towards continual reinforcement learning: A review and perspectives,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.889697Z

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Observation 767cd7da-3e4b-4ec3-9ca6-306eb626a448 · outbound

This paper cites Pseudo- rehearsal: Achieving deep reinforcement learning without catastrophic forgetting,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Pseudo- rehearsal: Achieving deep reinforcement learning without catastrophic forgetting,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.878701Z

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Observation ce6d6447-4429-4c24-9de1-43d07e919041 · outbound

This paper cites Under- standing the impact of entropy on policy optimization,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Under- standing the impact of entropy on policy optimization,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.862612Z

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Observation b3049bf6-5910-4679-b4a1-0e0c75349801 · outbound

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

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Offline reinforcement learning with implicit q-learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.848172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 254499a5-58f1-44ea-b094-642718decc2c · outbound

This paper cites Monotonic quantile network for worst-case offline reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Monotonic quantile network for worst-case offline reinforcement learning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.821851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6a521abc-f9b7-4a8c-8d51-deaeb70994ea · outbound

This paper cites Mild policy evaluation for offline actor–critic,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Mild policy evaluation for offline actor–critic,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.802096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9b1aa0a2-89b5-4c00-b1fe-a64778406f1a · outbound

This paper cites Plas: Latent action space for offline reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Plas: Latent action space for offline reinforcement learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.786108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 93f6a1e6-2fc9-4009-8b77-8c92d1cbe2b2 · outbound

This paper cites Offline re- inforcement learning with fisher divergence critic regularization,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Offline re- inforcement learning with fisher divergence critic regularization,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.771233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5cab6046-fd92-4aeb-b32b-ffa60db29e85 · outbound

This paper cites A minimalist approach to offline reinforce- ment learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning A minimalist approach to offline reinforce- ment learning,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.754517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ed97ed5c-5052-4387-b2c6-4e69babe4d73 · outbound

This paper cites An emphatic approach to the problem of off-policy temporal-difference learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning An emphatic approach to the problem of off-policy temporal-difference learning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.738232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5a490ac3-3716-4082-b839-5b668d699a9a · outbound

This paper cites AlgaeDICE: Policy Gradient from Arbitrary Experience.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning AlgaeDICE: Policy Gradient from Arbitrary Experience

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.710374Z

Source-reported events for the cited work

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Observation 541808ba-a711-488d-90d7-f249620443ae · outbound

This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.715755Z

Source-reported events for the cited work

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Observation 73c47f21-f045-4efe-bf44-27de5bc11b19 · outbound

This paper cites Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.720049Z

Source-reported events for the cited work

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Observation 47ed3831-d768-40ce-999d-baef74418efb · outbound

This paper cites Safe policy improvement with an estimated baseline policy,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Safe policy improvement with an estimated baseline policy,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.717704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dad1690d-e67f-4f54-ac3b-b726d033ab6e · outbound

This paper cites An optimistic perspec- tive on offline reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning An optimistic perspec- tive on offline reinforcement learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.695468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 18a22e3b-b762-4a24-bb5e-80cd10656329 · outbound

This paper cites S4rl: Surprisingly simple self- supervision for offline reinforcement learning in robotics,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning S4rl: Surprisingly simple self- supervision for offline reinforcement learning in robotics,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.676761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d7fa561e-e905-42e9-ab34-4a89264a1b8e · outbound

This paper cites Offline rl without off-policy evaluation,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Offline rl without off-policy evaluation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.658956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b35b240d-a462-4756-8444-40ff5f863a1f · outbound

This paper cites Offline reinforcement learning as one big sequence modeling problem,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Offline reinforcement learning as one big sequence modeling problem,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.644273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 54e31358-01ff-4a07-a407-1eab4d30af00 · outbound

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

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Decision transformer: Reinforcement learning via sequence modeling,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.621688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ae7dcb9e-99ae-42ca-a716-b3a7a983aa05 · outbound

This paper cites Attention is all you need,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Attention is all you need,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.756293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1df9141-9012-4747-806c-9d0e54530508 · outbound

This paper cites Hundreds guide millions: Adaptive offline reinforcement learning with expert guidance,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Hundreds guide millions: Adaptive offline reinforcement learning with expert guidance,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.595317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0196fc0a-8a7e-4e74-81c2-a4a5369ae92f · outbound

This paper cites When to trust your model: Model-based policy optimization,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning When to trust your model: Model-based policy optimization,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.580641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3e182e6b-f71e-46a8-bb2c-522e9637557f · outbound

This paper cites Morel: Model-based offline reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Morel: Model-based offline reinforcement learning,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.562204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ac1efbb6-959d-41f9-af68-6f42f61064b5 · outbound

This paper cites Mopo: Model-based offline policy optimization,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Mopo: Model-based offline policy optimization,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.541462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 464d5d27-c0f5-4b6b-a00d-c4f711e32e55 · outbound

This paper cites Deployment-efficient reinforcement learning via model-based offline optimization,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Deployment-efficient reinforcement learning via model-based offline optimization,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.523716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c949a1f1-dfe9-442b-b700-b029becd25fd · outbound

This paper cites Agnostic system identification for model- based reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Agnostic system identification for model- based reinforcement learning,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.511346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0750ce75-a3e2-4eac-b255-041a0844acd3 · outbound

This paper cites Synthesis and stabilization of complex behaviors through online trajectory optimization,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Synthesis and stabilization of complex behaviors through online trajectory optimization,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.488480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b83b006e-6a47-4a97-b4b3-88496825af52 · outbound

This paper cites A survey of monte carlo tree search methods,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning A survey of monte carlo tree search methods,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.472278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e5e23dc8-9c61-4eca-8f3b-be61c896e3d9 · outbound

This paper cites Near-optimal regret bounds for reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Near-optimal regret bounds for reinforcement learning,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.453522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bf9156e5-3646-4a9f-ac3c-a83fc7bdcc34 · outbound

This paper cites Deep exploration via bootstrapped dqn,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Deep exploration via bootstrapped dqn,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.428736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a06c13aa-1ede-4dbb-af80-10cfe8f6a11d · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Semi-supervised classification with graph convolutional networks,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.825283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1f40be4-19a9-4df4-9c75-102fda8a9fd9 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Deep Learning using Rectified Linear Units (ReLU)

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.830698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ef72819-0e7d-4caf-abac-f05f95eab1f9 · outbound

This paper cites D4rl: Datasets for deep data-driven reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning D4rl: Datasets for deep data-driven reinforcement learning,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.836252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cd85f12-5e18-43f0-bc55-655bdc2ce90f · outbound

This paper cites Mujoco: A physics engine for model-based control,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Mujoco: A physics engine for model-based control,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.387727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 574e91e4-306d-44de-89a2-4996866dbcf3 · outbound

This paper cites OpenAI Gym.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning OpenAI Gym

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.850772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af6916ec-8898-4c75-8cc9-c81d9a8a56ce · outbound

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

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Learning complex dexterous manipulation with deep reinforcement learning and demonstrations,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.375866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8c6bc3c8-eed9-4a2a-a55f-fef9217f70e2 · outbound

This paper cites Alvinn: An autonomous land vehicle in a neural network,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Alvinn: An autonomous land vehicle in a neural network,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.361770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 53840c34-4bc8-42df-acb7-f53d277e0c31 · outbound

This paper cites Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.874088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e27b303-fd48-4f7a-ab0e-5f3104b3219b · outbound

This paper cites A Workflow for Offline Model-Free Robotic Reinforcement Learning.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning A Workflow for Offline Model-Free Robotic Reinforcement Learning

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.880729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 548014ce-ca7f-43a7-892d-8c1afd42e30c · outbound

This paper cites Relay pol- icy learning: Solving long horizon tasks via imitation and reinforcement learning,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Relay pol- icy learning: Solving long horizon tasks via imitation and reinforcement learning,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.344721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2ae1ff18-92e9-4440-b0d7-55972b6bcb1e · outbound

This paper cites an unresolved cited work.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Unresolved cited work

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:41.891069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 160307f8-775d-4ca5-8bd4-3c94906f7760 · outbound

This paper cites Stability analysis of discrete-time infinite-horizon optimal control with discounted cost,.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning Stability analysis of discrete-time infinite-horizon optimal control with discounted cost,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.313359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3af05d7b-1290-4ff3-a114-3b8248558388 · outbound

This paper cites >" to " <.

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning >" to " <

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:42.293002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers