Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T19:03:10.619048Z
Paper Citation Record · LEDGER
As of 24 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 0 inbound Pith citation observations for arXiv:2411.11088.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T19:03:10.619048Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
71 of 71 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 749f8502-9f40-4b75-94d9-ed3dc1a3c397 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Uncertainty-based offline reinforcement learning with diversified Q-ensemble
Reference 1
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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Model-based offline planning
Reference 2
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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Pessimistic bootstrapping for uncertainty-driven offline reinforcement learning
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Observation f582a85d-8be9-4099-b0a9-db6f6e72ab3c · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning
Reference 4
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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Offline RL without off-policy evaluation
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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Q-transformer: Scalable offline reinforcement learning via autoregressive Q-functions
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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces The dynamics of reinforcement learning in cooperative multiagent systems
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Observation 6b3a75aa-9c8d-4287-9ec4-cc90a1b104ea · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Deep visual reasoning - learning to predict action sequences for assembly tasks
Reference 9
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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Value function factorization with dynamic weighting for deep multi-agent reinforcement learning
Reference 10
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Observation bd86cd62-fd5d-4cf3-9bf3-a3965773e0da · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Deep Reinforcement Learning in Large Discrete Action Spaces
Reference 11
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Observation dfdd3a67-a0fa-4438-a90f-123165eeacc2 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Growing action spaces
Reference 12
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Observation 87abe69c-51b6-4503-9b57-2b0ae7c081f2 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces D4RL: Datasets for Deep Data-Driven Reinforcement Learning
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Observation 6bbf57ef-247e-4efc-b255-30c90bb31930 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces A minimalist approach to offline reinforcement learning
Reference 14
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Observation a1bb01bb-442b-4914-b687-e5edcb645c1a · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Addressing function approximation error in actor-critic methods
Reference 15
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Observation 10774b93-9586-4dc5-adb2-f478da525fe6 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Benchmarking Batch Deep Reinforcement Learning Algorithms
Reference 16
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Observation 7cfbce22-5b08-4573-b027-9c73c663fb99 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Off-policy deep reinforcement learning without exploration
Reference 17
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Observation 926af992-c702-4b00-9ccc-4afdc17cea4e · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Q-learning for robot control
Reference 18
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Observation 25c2fd69-746b-4dcc-91c0-a7000721a559 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Why so pessimistic? estimating uncertainties for offline RL through ensembles, and why their independence matters
Reference 19
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Observation 9a57e88f-3bae-4844-8496-7a3b9c418607 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Reference 20
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Observation 253796a6-176b-407c-89ea-792783f1775f · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Learning pseudometric-based action representations for offline reinforcement learning
Reference 21
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Observation 67773c32-af0b-416f-9d0e-170149a9d35e · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Efficient solution algorithms for factored MDPs
Reference 22
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Observation cc55722f-4388-4a5e-ade7-b53c3857aaec · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Addressing extrapolation error in deep offline reinforcement learning
Reference 23
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Observation 89c9d8f8-66ef-4572-9b9f-5106a487e404 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Rl unplugged: A suite of benchmarks for offline reinforcement learning
Reference 24
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Observation 2a5989c3-19df-4335-b0a6-c03680d7a1ba · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Rainbow: Combining improvements in deep reinforcement learning
Reference 25
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Observation 84a67943-1776-4f65-85bf-dfea4baa8eaf · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Distributed prioritized experience replay
Reference 26
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Observation df5e5766-7fac-464a-8fdc-d55a651b915f · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Learning and planning in complex action spaces
Reference 27
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Observation 9b3fe3ab-0316-4d50-a870-f038da66ad7c · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Revalued: Regularised ensemble value-decomposition for factorisable Markov decision processes
Reference 28
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Observation 416971c4-2da5-42c9-8d1c-4eeb657dedc8 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Planning with diffusion for flexible behavior synthesis
Reference 29
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Observation babaabdb-4031-4cda-bd8b-5b7356c91b11 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Scalable deep reinforcement learning for vision-based robotic manipulation
Reference 30
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Observation 21b95b16-5269-4173-9b8b-45194f4789f2 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Efficient reinforcement learning in factored MDPs
Reference 31
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Observation 626988be-1b28-471c-a03d-b1f1d461101f · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces MOReL : Model-based offline reinforcement learning
Reference 32
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Observation 0a94f202-da42-486c-9de2-492ac413f4cb · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Adam: A Method for Stochastic Optimization
Reference 33
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Observation e714f0af-3431-4f31-879c-41a872cae939 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Al Sallab, Senthil Yogamani, and Patrick Pérez
Reference 34
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Observation 3d7a6251-7510-4dbd-9a5a-2f97013aa336 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Offline reinforcement learning with Fisher divergence critic regularization
Reference 35
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Observation 58fb6db3-dc3c-4ee4-b487-cf6267e6315b · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Offline reinforcement learning with implicit Q-Learning
Reference 36
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Observation e0c2110a-bfe4-4ff2-954f-31375e7c328e · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Multi-agent reinforcement learning as a rehearsal for decentralized planning
Reference 37
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Observation 0fe6e26c-5fe5-4392-a2a4-51790e31eb46 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Stabilizing off-policy Q-learning via bootstrapping error reduction
Reference 38
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Observation 6397d9d5-6d8b-419d-be67-816ab20c37f7 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Conservative Q-learning for offline reinforcement learning
Reference 39
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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Batch reinforcement learning
Reference 40
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Observation 92ab5873-4f1f-4cea-bb2f-3b4fcc7727e4 · outbound
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Reference 41
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Observation d0492a9d-cff5-476e-a5cf-41f08b30b80c · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Efficient large-scale fleet management via multi-agent deep reinforcement learning
Reference 42
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Observation eaac4c8f-7871-4b17-9b51-0323444cf9f6 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Reinforcement learning for clinical decision support in critical care: comprehensive review
Reference 43
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Observation aa51d450-5569-4890-887a-d9b9c2f8a8e7 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Action-quantized offline reinforcement learning for robotic skill learning
Reference 44
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Observation 89284f3e-4c10-46e3-a682-0db39c9ca5de · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Benchmarking reinforcement learning algorithms on real-world robots
Reference 45
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Observation cd53f65b-3894-4e0e-bb6b-debdafa96aad · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Discrete Sequential Prediction of Continuous Actions for Deep RL
Reference 46
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Observation 926ff78f-ebe2-4c76-89cf-6cd8f2e81b16 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Playing Atari with Deep Reinforcement Learning
Reference 47
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Observation 7b7926a9-c6e7-4acf-bb96-c22c647a7f43 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Anti-exploration by random network distillation
Reference 48
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Observation da00f59f-5e12-470d-a0a4-8442804b6696 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Factored action spaces in deep reinforcement learning
Reference 49
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Observation 6549ffa9-b9d2-4dc4-a43d-8dbf0ffc9911 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning
Reference 50
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Observation e319e4c0-9e27-4c1e-9055-4699a7e52e34 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Monotonic value function factorisation for deep multi-agent reinforcement learning
Reference 51
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Observation de7082fb-8e4c-450c-9f41-e621efe6ba36 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Leveraging factored action spaces for off-policy evaluation
Reference 52
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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Is bang-bang control all you need? solving continuous control with Bernoulli policies
Reference 53
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Reference 54
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Reference 55
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Observation 62c0ad27-6f49-4bfb-ab0a-8cace85207dd · outbound
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Reference 56
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Reference 57
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Observation b53450e0-fc4a-4d63-936e-84aac85b1e28 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Overcoming model bias for robust offline deep reinforcement learning
Reference 58
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Observation b0afe93c-5661-49a1-b6ec-7a8452097a63 · outbound
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Reference 59
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Reference 60
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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Action branching architectures for deep reinforcement learning
Reference 61
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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Issues in using function approximation for reinforcement learning
Reference 62
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Observation 4e45167d-b1ad-4371-a45d-8154233ba893 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces dm\_control: Software and tasks for continuous control
Reference 63
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Reference 64
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Observation 48dd1f62-717e-45c1-bec5-08e23a57de46 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Behavior Regularized Offline Reinforcement Learning
Reference 65
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Observation bf18b59d-b88f-48ce-ada5-e298676a5bd3 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces RORL : Robust offline reinforcement learning via conservative smoothing
Reference 66
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Observation 7177f340-7afd-4c85-adfc-fbda40a250a6 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Reinforcement learning in healthcare: A survey
Reference 67
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Observation d5c1c604-2253-4a91-814f-4bba423b7656 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces MOPO : Model-based offline policy optimization
Reference 68
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Observation 68c1c6c0-0f34-4fc2-94bc-e14ec62c22e0 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces COMBO : Conservative offline model-based policy optimization
Reference 69
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Observation 91331d7c-df83-4dd3-8aad-f7f0fa78d947 · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Deep reinforcement learning for page-wise recommendations
Reference 70
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Observation ba122343-154d-4eb4-838e-b2460cc18d8e · outbound
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces PLAS : Latent action space for offline reinforcement learning
Reference 71
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No inbound Pith citation observations are available.