Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis | Paper Citation Record

Observation 5c895a0b-76a4-440d-8973-a3d0e758408f · outbound

This paper cites Improved algorithms for linear stochastic bandits.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Improved algorithms for linear stochastic bandits

Reference 1

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Observation c98ec8b3-3062-4b4e-8bd9-9074a259a4ab · outbound

This paper cites Analysis of thompson sampling for the multi-armed bandit problem.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Analysis of thompson sampling for the multi-armed bandit problem

Reference 2

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Observation dbd2d4cd-c320-46f3-a782-489dfb43dccf · outbound

This paper cites Thompson sampling for contextual bandits with linear payoffs.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Thompson sampling for contextual bandits with linear payoffs

Reference 3

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Observation e1ed65f2-ecaa-4a58-9c66-3dbd4c185620 · outbound

This paper cites Optimal Best-Arm Identification in Bandits with Access to Offline Data.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Optimal Best-Arm Identification in Bandits with Access to Offline Data

Reference 4

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Observation fb1ea420-bc2c-40a9-8b9f-0a2a64753cc7 · outbound

This paper cites Exploration--exploitation tradeoff using variance estimates in multi-armed bandits.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Exploration--exploitation tradeoff using variance estimates in multi-armed bandits

Reference 5

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Observation 01e262d1-166e-40a0-9384-e84988a08afc · outbound

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Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Unresolved cited work

Reference 6

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Observation b9a12319-d135-458e-9167-e33af680928d · outbound

This paper cites Minimax regret bounds for reinforcement learning.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Minimax regret bounds for reinforcement learning

Reference 7

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Observation f44891fb-e7aa-4523-b982-6c7d55e07e19 · outbound

This paper cites Stochastic linear bandits robust to adversarial attacks.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Stochastic linear bandits robust to adversarial attacks

Reference 8

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Observation f2bb5257-693c-4215-80da-4b2166df8393 · outbound

This paper cites Offline contextual bandits with overparameterized models.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Offline contextual bandits with overparameterized models

Reference 9

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Observation 6f739da6-b875-4918-a870-78e524e0581b · outbound

This paper cites Regret analysis of stochastic and nonstochastic multi-armed bandit problems.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Regret analysis of stochastic and nonstochastic multi-armed bandit problems

Reference 10

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Observation 92622cb2-85f6-4433-b05d-c2d61d0a5f07 · outbound

This paper cites Kullback-leibler upper confidence bounds for optimal sequential allocation.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Kullback-leibler upper confidence bounds for optimal sequential allocation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:31.715206Z

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Observation a0f51a63-b927-4230-a41c-a09f73794017 · outbound

This paper cites The Elliptical Potential Lemma Revisited.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis The Elliptical Potential Lemma Revisited

Reference 12

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Observation e6ef10ea-4654-4bf6-880f-6913f90944ba · outbound

This paper cites An empirical evaluation of thompson sampling.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis An empirical evaluation of thompson sampling

Reference 13

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Observation 49cc509e-74a7-455c-adfd-0ed56063cc20 · outbound

This paper cites Information-theoretic considerations in batch reinforcement learning.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Information-theoretic considerations in batch reinforcement learning

Reference 14

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Observation c42f4de1-f0de-40c9-89d5-596efbef1efa · outbound

This paper cites Leveraging (biased) information: Multi-armed bandits with offline data.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Leveraging (biased) information: Multi-armed bandits with offline data

Reference 15

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Observation 0d4484bb-ebcd-47bf-ac44-dc2c28e5298d · outbound

This paper cites Contextual bandits with linear payoff functions.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Contextual bandits with linear payoff functions

Reference 16

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Observation 4f273f8b-d01d-4bb3-8e7c-f73427d23de8 · outbound

This paper cites Stochastic linear optimization under bandit feedback.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Stochastic linear optimization under bandit feedback

Reference 17

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Observation 04905289-db52-4afe-b818-25bbb45f9a4a · outbound

This paper cites Minimax-optimal off-policy evaluation with linear function approximation.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Minimax-optimal off-policy evaluation with linear function approximation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:31.595939Z

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Observation 010d2342-131d-49e0-8545-81ebdaab5c71 · outbound

This paper cites The kl-ucb algorithm for bounded stochastic bandits and beyond.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis The kl-ucb algorithm for bounded stochastic bandits and beyond

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:31.570089Z

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Observation 1eda491f-0c24-472c-ac81-c687a01adbb1 · outbound

This paper cites Guidelines for reinforcement learning in healthcare.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Guidelines for reinforcement learning in healthcare

Reference 20

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Observation 5ea1c3bb-61bf-45f5-b3c4-c629bc3c2ccc · outbound

This paper cites The movielens datasets: History and context.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis The movielens datasets: History and context

Reference 21

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Observation b265bfe8-4724-46cf-9f59-ffb10609395e · outbound

This paper cites A reduction from linear contextual bandits lower bounds to estimations lower bounds.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis A reduction from linear contextual bandits lower bounds to estimations lower bounds

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:31.483752Z

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Observation fe546f33-8415-4f92-993b-e61d5dfc2339 · outbound

This paper cites Deep q-learning from demonstrations.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Deep q-learning from demonstrations

Reference 23

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Observation 4d80e72d-04bd-4be8-ab07-65ff2124aa24 · outbound

This paper cites Optimal best-arm identification in linear bandits.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Optimal best-arm identification in linear bandits

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:29.433662Z

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Observation 582dc0a1-c169-4ace-9248-d63eb78cc5ae · outbound

This paper cites Provably efficient reinforcement learning with linear function approximation.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Provably efficient reinforcement learning with linear function approximation

Reference 25

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Observation 549a36c1-cb33-402e-a0db-ded1c5cc646a · outbound

This paper cites Bellman eluder dimension: New rich classes of rl problems, and sample-efficient algorithms.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Bellman eluder dimension: New rich classes of rl problems, and sample-efficient algorithms

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:31.398827Z

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Observation eb141d2d-c21a-4f56-a2aa-89f723036659 · outbound

This paper cites Is pessimism provably efficient for offline rl? In International Conference on Machine Learning, pages 5084--5096.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Is pessimism provably efficient for offline rl? In International Conference on Machine Learning, pages 5084--5096

Reference 27

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Observation e7ac0e9b-76de-4530-9ae9-4ef74f1c9216 · outbound

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

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Conservative q-learning for offline reinforcement learning

Reference 28

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Observation 9dd65cf7-1b34-4045-80ca-b603d1c277c5 · outbound

This paper cites Asymptotically efficient adaptive allocation rules.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Asymptotically efficient adaptive allocation rules

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:31.323438Z

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Observation 48770e6b-2a6e-4cbd-a145-19f174a6274f · outbound

This paper cites Bandit algorithms.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Bandit algorithms

Reference 30

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Observation 92c2d990-9539-41b7-8a02-2a9f06ad463e · outbound

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

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Offline-to-online reinforcement learning via balanced replay and pessimistic q-ensemble

Reference 31

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Observation 1fd1f3f3-1d2e-4905-9663-0cde19d7c110 · outbound

This paper cites Lee, Yuejie Chi, and Yuxin Chen.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Lee, Yuejie Chi, and Yuxin Chen

Reference 32

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Observation 44abf8ef-5c8d-4d7b-bf1a-d4459351a26a · outbound

This paper cites Settling the sample complexity of model-based offline reinforcement learning.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Settling the sample complexity of model-based offline reinforcement learning

Reference 33

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Observation 6b53940f-e37c-4738-a6b0-c4f072530764 · outbound

This paper cites Pessimism for offline linear contextual bandits using l _p confidence sets.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Pessimism for offline linear contextual bandits using l _p confidence sets

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:31.208203Z

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Observation e61f07a1-673f-495b-88fb-876bcfaadb62 · outbound

This paper cites A contextual-bandit approach to personalized news article recommendation.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis A contextual-bandit approach to personalized news article recommendation

Reference 35

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Observation 987369e1-50d9-462b-ab7c-2165fc6a5f6b · outbound

This paper cites Fast active learning for pure exploration in reinforcement learning.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Fast active learning for pure exploration in reinforcement learning

Reference 36

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Observation f557d306-35f1-454a-b0b5-d501c09e3a6d · outbound

This paper cites Efficient memory-based learning for robot control.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Efficient memory-based learning for robot control

Reference 37

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Observation 548bff10-8852-4329-83df-625b62a4ff98 · outbound

This paper cites Collaborative-filtering.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Collaborative-filtering

Reference 38

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Observation c9ac7849-878d-47fc-a732-d1156fe9ac2f · outbound

This paper cites Finite-time bounds for fitted value iteration.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Finite-time bounds for fitted value iteration

Reference 39

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Observation 70166982-94ee-4140-930b-0fbe349720a1 · outbound

This paper cites Overcoming exploration in reinforcement learning with demonstrations.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Overcoming exploration in reinforcement learning with demonstrations

Reference 40

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Observation f668b8b1-7887-44cc-91b5-c1c598401423 · outbound

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

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 41

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Observation a68c2aa5-0991-45f9-96ce-9013e9e23a41 · outbound

This paper cites Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization

Reference 42

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Observation 42d21d93-8346-44dc-a5be-0bd20480f589 · outbound

This paper cites Cutting to the chase with warm-start contextual bandits.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Cutting to the chase with warm-start contextual bandits

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:31.008409Z

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Observation a23cd302-2197-4e6e-a6d0-ab54448f3761 · outbound

This paper cites Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Reference 44

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Observation c2f7c428-45d9-4047-8262-7d8052ce2572 · outbound

This paper cites Bridging offline reinforcement learning and imitation learning: A tale of pessimism.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Bridging offline reinforcement learning and imitation learning: A tale of pessimism

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.981176Z

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Observation 6c010a11-7c86-4337-8617-ef321ed93183 · outbound

This paper cites Agnostic System Identification for Model-Based Reinforcement Learning.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Agnostic System Identification for Model-Based Reinforcement Learning

Reference 46

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Observation ef0b3f80-f1e1-4e99-989f-385b3fdab78b · outbound

This paper cites Bandits with Mean Bounds.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Bandits with Mean Bounds

Reference 47

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Observation ec621217-6fcc-48d3-98c6-c099f076e4ee · outbound

This paper cites Multi-armed bandit problems with history.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Multi-armed bandit problems with history

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.946397Z

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 796f590c-eb96-45de-829c-327756193768 · outbound

This paper cites User cold-start problem in multi-armed bandits: When the first recommendations guide the user’s experience.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis User cold-start problem in multi-armed bandits: When the first recommendations guide the user’s experience

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.909059Z

Source-reported events for the cited work

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Observation 072ccc22-bb4b-4321-8cbd-af8035632d12 · outbound

This paper cites Best-arm identification in linear bandits.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Best-arm identification in linear bandits

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.869269Z

Source-reported events for the cited work

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Observation 905dd96b-4a67-4054-979a-a5edb08b2da2 · outbound

This paper cites Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 51

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Observation d557b812-f358-4906-9f25-cbfe7e9a1778 · outbound

This paper cites an unresolved cited work.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:21:30.840977Z

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Observation ef0ef3db-819e-4f9f-995d-2cdc6b32b605 · outbound

This paper cites Algorithms for reinforcement learning.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Algorithms for reinforcement learning

Reference 53

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Observation 4fabacf2-bb9e-4fca-b173-82e627b3c8fc · outbound

This paper cites A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:21:30.160097Z

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Observation 55fb2b55-bc45-4ca8-842e-fe744e94b712 · outbound

This paper cites Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs

Reference 55

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Observation df602222-d459-41ae-9e10-d48e203e50b1 · outbound

This paper cites Predictive off-policy policy evaluation for nonstationary decision problems, with applications to digital marketing.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Predictive off-policy policy evaluation for nonstationary decision problems, with applications to digital marketing

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.799656Z

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Observation b0332b30-f19e-40ce-8a94-878cb945dace · outbound

This paper cites On the likelihood that one unknown probability exceeds another in view of the evidence of two samples.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis On the likelihood that one unknown probability exceeds another in view of the evidence of two samples

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:29.705219Z

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Observation f3f4b2b8-416b-4af7-97f8-942f265df193 · outbound

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

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:29.713695Z

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Observation a9c0dbe8-6e5e-4322-9442-971d1fa2aae3 · outbound

This paper cites Pessimistic model-based offline reinforcement learning under partial coverage.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Pessimistic model-based offline reinforcement learning under partial coverage

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.753059Z

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Observation 3cac87ee-b04c-4e77-b05d-d5f45757dcd9 · outbound

This paper cites Representation Learning for Online and Offline RL in Low-rank MDPs.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Representation Learning for Online and Offline RL in Low-rank MDPs

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:29.732840Z

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Observation 87db7217-299f-4cca-b768-227f46ae0dfb · outbound

This paper cites Instance-dependent near-optimal policy identification in linear mdps via online experiment design.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Instance-dependent near-optimal policy identification in linear mdps via online experiment design

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:29.744641Z

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Observation f508c3f2-438e-48e1-b5da-9d5d4cee3c12 · outbound

This paper cites Leveraging offline data in online reinforcement learning.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Leveraging offline data in online reinforcement learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.710969Z

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

Observation 685e482b-1df0-4cd1-9d5c-2a4c6f90efbd · outbound

This paper cites Experimental design for regret minimization in linear bandits.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Experimental design for regret minimization in linear bandits

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.689024Z

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Observation 09163372-f577-437b-8cb6-8605aac2909b · outbound

This paper cites Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:21:30.031698Z

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

Observation 0716a315-be0d-4d68-8c23-e9ece9cd99ac · outbound

This paper cites Bellman-consistent pessimism for offline reinforcement learning.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Bellman-consistent pessimism for offline reinforcement learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.665695Z

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 d779f11e-5fb4-4a27-b7e3-099c6753d832 · outbound

This paper cites Policy finetuning: Bridging sample-efficient offline and online reinforcement learning.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Policy finetuning: Bridging sample-efficient offline and online reinforcement learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.642102Z

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 ec161056-9f73-4450-8caf-ccb1b88a4a80 · outbound

This paper cites Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:29.794976Z

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Unavailable: canonical work link unavailable.

Observation e7181073-99bf-43f7-83f0-2b3174dbdbba · outbound

This paper cites Minimax optimal fixed-budget best arm identification in linear bandits.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Minimax optimal fixed-budget best arm identification in linear bandits

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:29.801662Z

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Unavailable: canonical work link unavailable.

Observation 5d7e2902-3ce0-4eec-bcd5-fe73934058d7 · outbound

This paper cites Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:21:29.968928Z

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 ff14bda5-a614-463d-96cc-aba9a321377b · outbound

This paper cites Provable benefits of actor-critic methods for offline reinforcement learning.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Provable benefits of actor-critic methods for offline reinforcement learning

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.605103Z

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 4af4ccfb-fb8b-4844-9c47-dd6400cd56b6 · outbound

This paper cites Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:29.829348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c67f8696-c5ea-46ea-ab54-995e8465a604 · outbound

This paper cites @esa (Ref.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis @esa (Ref

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:29.840130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdcc5227-6b28-4a7b-9012-800c7f84a40a · outbound

This paper cites an unresolved cited work.

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:29.850447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9079adcb-e6d0-4853-b3f4-ae45c8c1c524 · outbound

This paper cites page @startpage numbered @text Submitted to @long ( @short).

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis page @startpage numbered @text Submitted to @long ( @short)

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:30.535409Z

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