Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-16T04:47:05.795761Z
Paper Citation Record · LEDGER
As of 17 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 5 inbound Pith citation observations for arXiv:2505.00663.
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 2026-08-16T04:47:05.795761Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-29T22:19:53.934535Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T06:56:44.503712Z
54 of 54 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation b5b7dfde-d8ef-4053-a00f-f73925c912b2 · outbound
Wasserstein Policy Optimization write newline
Reference 1
Source-reported events for the cited work
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Observation 2254d3da-e420-4c79-b6aa-9e78c980eec9 · outbound
Wasserstein Policy Optimization T., Tassa, Y., Munos, R., Heess, N., and Riedmiller, M
Reference 2
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Observation 5ab9bc6b-cab4-4dbc-b48b-8645b4b1b909 · outbound
Wasserstein Policy Optimization Wasserstein Robust Reinforcement Learning
Reference 3
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Observation 733ba229-d450-4089-88ae-ba561c450cbc · outbound
Wasserstein Policy Optimization M., Lee, J
Reference 4
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Observation d34dd838-1579-4ca8-aba0-b935f4832c21 · outbound
Wasserstein Policy Optimization Gradient flows: in metric spaces and in the space of probability measures
Reference 5
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Unavailable: canonical work link unavailable.
Observation 56df3e18-0b0e-4184-b5d8-f649980ff82f · outbound
Wasserstein Policy Optimization W., Budden, D., Dabney, W., Horgan, D., Tb, D., Muldal, A., Heess, N., and Lillicrap, T
Reference 6
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Observation 41dad690-926f-4135-b459-184cc07b59cb · outbound
Wasserstein Policy Optimization G., Sutton, R
Reference 7
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Observation c109af0b-e5a3-41d2-a497-fa0e246179c5 · outbound
Wasserstein Policy Optimization G., Dabney, W., and Munos, R
Reference 8
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Observation 9662de86-3d7e-48cc-930a-e1e12a352561 · outbound
Wasserstein Policy Optimization and Brenier, Y
Reference 9
Source-reported events for the cited work
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Observation d8ac57cd-5b35-4274-82fb-516c7af50691 · outbound
Wasserstein Policy Optimization Development of free-boundary equilibrium and transport solvers for simulation and real-time interpretation of tokamak experiments
Reference 10
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Observation 59307d2d-0cea-46e5-806f-be758d7ddaf0 · outbound
Wasserstein Policy Optimization MICo: Improved representations via sampling-based state similarity for Markov decision processes
Reference 11
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Observation 5b4cddc1-b794-4c27-9d69-bffcd7d184c8 · outbound
Wasserstein Policy Optimization T., Rubanova, Y., Bettencourt, J., and Duvenaud, D
Reference 12
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Observation c38ecf22-afac-4732-8aba-d3fcf077b551 · outbound
Wasserstein Policy Optimization Fast and accurate deep network learning by exponential linear units (elus)
Reference 13
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Observation 6f464a34-6fdf-406c-87b2-0e9a0a2d4222 · outbound
Wasserstein Policy Optimization Magnetic control of tokamak plasmas through deep reinforcement learning
Reference 14
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Observation 0495d737-0898-4b96-bce4-c7eec5c1cbae · outbound
Wasserstein Policy Optimization Experimental research on the TCV tokamak
Reference 15
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Observation 3a3fc4cf-9920-41a2-b044-ee578f2224fb · outbound
Wasserstein Policy Optimization Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Reference 16
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Observation 2ec59336-bd68-4aaf-a304-ee1b46abba65 · outbound
Wasserstein Policy Optimization CALE: Continuous Arcade Learning Environment
Reference 17
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Observation b7d4ecf8-0e0e-4cbb-b112-33cb32583843 · outbound
Wasserstein Policy Optimization Metrics for finite markov decision processes
Reference 18
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Observation 6335febe-a02a-473e-9ae8-dedb8a0a42fe · outbound
Wasserstein Policy Optimization Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Reference 19
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Observation 27536610-f19e-42b6-8a56-2f38f5d39127 · outbound
Wasserstein Policy Optimization H., Tirumala, D., Humplik, J., Wulfmeier, M., Tunyasuvunakool, S., Siegel, N
Reference 20
Source-reported events for the cited work
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Observation 857f1670-9d4e-432b-99e7-5be13481fc37 · outbound
Wasserstein Policy Optimization Wasserstein Unsupervised Reinforcement Learning
Reference 21
Source-reported events for the cited work
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Observation 744ec0f7-4171-4892-9043-dd086adef2b2 · outbound
Wasserstein Policy Optimization Learning continuous control policies by stochastic value gradients
Reference 22
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Observation 2fd903b9-9f74-4b6a-b4c6-bf191014cea0 · outbound
Wasserstein Policy Optimization Acme: A Research Framework for Distributed Reinforcement Learning
Reference 23
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Observation 0395637c-0a5c-4c90-9a66-d09ac040974d · outbound
Wasserstein Policy Optimization Unresolved cited work
Reference 24
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Observation 45aaa500-a603-4a85-a431-74aea021f4e5 · outbound
Wasserstein Policy Optimization Unresolved cited work
Reference 25
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Observation b48467e3-ffd7-45c6-a333-91bda38df65b · outbound
Wasserstein Policy Optimization Categorical reparameterization with G umbel-softmax
Reference 26
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Observation acc2e2a9-bed6-428e-bb47-9446929ffd68 · outbound
Wasserstein Policy Optimization Unresolved cited work
Reference 27
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Observation 51a4ad7c-dfc5-4b11-af34-2432dd3181ce · outbound
Wasserstein Policy Optimization Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control
Reference 28
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Observation 683d7e2a-d004-4fca-a499-b7fcbeebd1f1 · outbound
Wasserstein Policy Optimization Continuous control with deep reinforcement learning
Reference 29
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Observation fa09afc5-6421-442b-89e4-cf543840fdda · outbound
Wasserstein Policy Optimization J., Mnih, A., and Teh, Y
Reference 30
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Observation 9850b65c-b8ab-4482-8108-b8e4a356824a · outbound
Wasserstein Policy Optimization and Grosse, R
Reference 31
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Observation ac03dda1-012e-4034-95eb-2c6f0a8b5bb0 · outbound
Wasserstein Policy Optimization M., Likmeta, A., and Restelli, M
Reference 32
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Observation e144be77-e467-494d-945b-4a65c3f7f94a · outbound
Wasserstein Policy Optimization Efficient Wasserstein Natural Gradients for Reinforcement Learning
Reference 33
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Observation afd51786-a8c3-48b1-b167-0d308880fc81 · outbound
Wasserstein Policy Optimization Wasserstein quantum M onte C arlo: a novel approach for solving the quantum many-body schr \"o dinger equation
Reference 34
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Observation f9fe4337-2e4f-4197-ae50-0dc6f03d9957 · outbound
Wasserstein Policy Optimization Learning to score behaviors for guided policy optimization
Reference 35
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Observation 75eb8626-fb33-486c-b607-92acf35b5274 · outbound
Wasserstein Policy Optimization Revisiting Natural Gradient for Deep Networks
Reference 36
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Observation d6f13103-9698-4474-9d76-bc0048a81deb · outbound
Wasserstein Policy Optimization Unresolved cited work
Reference 37
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Observation 800c788b-db9b-4b7d-a572-59a67930c74c · outbound
Wasserstein Policy Optimization Unresolved cited work
Reference 38
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Observation 094c430b-f82f-4804-b88c-8209fbf8b329 · outbound
Wasserstein Policy Optimization Trust region policy optimization
Reference 39
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Observation e8ff4f7c-2665-4a90-8f0b-8d98c02f2f19 · outbound
Wasserstein Policy Optimization High-Dimensional Continuous Control Using Generalized Advantage Estimation
Reference 40
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Observation 368ea6c0-f820-49db-9008-38cc22728977 · outbound
Wasserstein Policy Optimization Proximal Policy Optimization Algorithms
Reference 41
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Observation 07849974-2fdd-4b13-8a43-d4366ea6a636 · outbound
Wasserstein Policy Optimization Deterministic policy gradient algorithms
Reference 42
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Observation 49344668-057b-44f7-aa5c-cc03726391f9 · outbound
Wasserstein Policy Optimization V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
Reference 43
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Observation 6dee8b22-00dd-4fd2-953b-4b7fa8930f26 · outbound
Wasserstein Policy Optimization Unresolved cited work
Reference 44
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Observation e000d2e0-7711-4541-8ec6-49ec0d647327 · outbound
Wasserstein Policy Optimization S., McAllester, D., Singh, S., and Mansour, Y
Reference 45
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Observation e5e35a59-79ac-48c1-99df-a0c872ebd9c8 · outbound
Reference 46
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Observation f4975490-ef45-488d-9b33-09be25f5bf14 · outbound
Wasserstein Policy Optimization Mujoco: A physics engine for model-based control
Reference 47
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Observation 1b4fa37c-7da9-453d-82ff-66061c19dcde · outbound
Wasserstein Policy Optimization D., Michi, A., Chervonyi, Y., Davies, I., Paduraru, C., Lazic, N., Felici, F., Ewalds, T., Donner, C., Galperti, C., et al
Reference 48
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Observation 72b0774e-5686-4f6c-81fb-ee1b71650855 · outbound
Wasserstein Policy Optimization dm\_control: Software and tasks for continuous control
Reference 49
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Observation 76ec6004-2123-427e-af7e-1944d25931b7 · outbound
Wasserstein Policy Optimization Double q-learning
Reference 50
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Observation 3aa6ab08-bd3a-4050-a7d9-e48c39a41b19 · outbound
Wasserstein Policy Optimization and Wiering, M
Reference 51
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Observation 2014b134-8925-4098-b8d1-a254e7e549b1 · outbound
Wasserstein Policy Optimization Beyond regression: N ew tools for prediction and analysis in the behavioral sciences
Reference 52
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Observation 7ec0bc46-1d29-498e-8564-414468894dd6 · outbound
Wasserstein Policy Optimization Unresolved cited work
Reference 53
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Observation 8b443736-3676-4c3d-a32e-2b9135f53f77 · outbound
Wasserstein Policy Optimization Policy optimization as W asserstein gradient flows
Reference 54
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Observation ac0cc622-9671-4b93-b2be-8695092a6a38 · inbound
Challenges and opportunities for AI to help deliver fusion energy Wasserstein Policy Optimization
Reference 7
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Observation d42fbe4a-6cef-4066-bedb-7edae218c41c · inbound
A note on convergence of Wasserstein policy optimization Wasserstein Policy Optimization
Reference 2
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Observation 5238f7d7-76f5-44de-b82b-131b8f3d9832 · inbound
Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning Wasserstein Policy Optimization
Reference 11
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Observation 42fb7b7b-e288-4bc2-a5d4-6af7262093e9 · inbound
Ratio-Variance Regularized Policy Optimization Wasserstein Policy Optimization
Reference 9
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Observation 2aca0c6c-ab04-45a0-9c52-c2ef9587a81b · inbound
Policy Gradient for Continuous-Time Robust Markov Decision Processes Wasserstein Policy Optimization
Reference 25
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