Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-09T22:47:51.676289Z
Paper Citation Record · LEDGER
Mathematical methods of reinforcement learning
As of 18 August 2026, this Paper Citation Record lists 100 of 125 outbound references and 0 inbound Pith citation observations for arXiv:2607.06935.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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
Reference resolution
100 of 125 outbound references displayed
- verified exact15
- verified fuzzy79
- unresolved1
- parse uncertain2
- malformed identifier1
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 8c96fc50-d110-4b28-9264-fd9372aeb26c · outbound
This paper cites Puterman.Markov Decision Processes: Discrete Stochastic Dynamic Pro- gramming.
Mathematical methods of reinforcement learning Puterman.Markov Decision Processes: Discrete Stochastic Dynamic Pro- gramming
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.502493Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3840bc46-ce83-427a-8711-a6671b20ec41 · outbound
This paper cites The Landscape of Agentic Reinforcement Learning for LLMs: A Survey.
Mathematical methods of reinforcement learning The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T22:56:37.774476Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2bfeddfa-607d-448f-9da5-04af7a69f915 · outbound
This paper cites MIT press, 2022.
Mathematical methods of reinforcement learning MIT press, 2022
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.534166Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 607eb37a-f2f6-4e05-9e06-7f2f2c32d65d · outbound
This paper cites Deep belief markov models for pomdp inference.Neural networks, page 108386, 2025.
Mathematical methods of reinforcement learning Deep belief markov models for pomdp inference.Neural networks, page 108386, 2025
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.530914Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b7e8116b-faf7-47b4-86fc-94da8d8ad568 · outbound
This paper cites A markovian decision process.Journal of mathematics and me- chanics, 6(5):679–684, 1957.
Mathematical methods of reinforcement learning A markovian decision process.Journal of mathematics and me- chanics, 6(5):679–684, 1957
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.615465Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation dc7347e8-d4ba-4def-b8db-b5e8c793d64d · outbound
This paper cites An upper bound on the loss from approximate optimal-value functions.Machine Learning, 16(3):227–233, 1994.
Mathematical methods of reinforcement learning An upper bound on the loss from approximate optimal-value functions.Machine Learning, 16(3):227–233, 1994
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.621061Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8a84f11f-0456-4c3c-bdbf-91ffa2febd5a · outbound
This paper cites an unresolved cited work.
Mathematical methods of reinforcement learning Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-07-09T23:06:37.542427Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0a247d84-7460-4b2c-a4ed-fa142a938d6b · outbound
This paper cites Improved and generalized upper bounds on the complexity of policy iteration.Advances in Neural Information Processing Systems, 26, 2013.
Mathematical methods of reinforcement learning Improved and generalized upper bounds on the complexity of policy iteration.Advances in Neural Information Processing Systems, 26, 2013
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.547431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 594cc6c6-d866-44d8-ad15-56587c71c4c0 · outbound
This paper cites Springer, 2018.
Mathematical methods of reinforcement learning Springer, 2018
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.569487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2d187ea6-6994-44fc-a1b1-2d32ad828c69 · outbound
This paper cites From Convex Optimization to MDPs: A Review of First-Order, Second-Order and Quasi-Newton Methods for MDPs.
Mathematical methods of reinforcement learning From Convex Optimization to MDPs: A Review of First-Order, Second-Order and Quasi-Newton Methods for MDPs
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T22:56:37.745852Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4e9e4919-ca7a-480d-899a-9bf521a6209d · outbound
This paper cites A method for solving the convex programming problem with con- vergence rate o (1/k2).
Mathematical methods of reinforcement learning A method for solving the convex programming problem with con- vergence rate o (1/k2)
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.600268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 50718e16-f93f-4a33-a320-008081266679 · outbound
This paper cites Springer Science & Business Media, 2013.
Mathematical methods of reinforcement learning Springer Science & Business Media, 2013
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T22:56:37.504613Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f6c4c399-d58c-4373-a8cc-19127ff8c3eb · outbound
This paper cites Some methods of speeding up the convergence of iteration methods.
Mathematical methods of reinforcement learning Some methods of speeding up the convergence of iteration methods
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.601878Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 46b874da-b796-45de-b692-1b04b4cb6653 · outbound
This paper cites A first-order approach to accelerated value iteration.Operations Research, 71(2):517–535, 2023.
Mathematical methods of reinforcement learning A first-order approach to accelerated value iteration.Operations Research, 71(2):517–535, 2023
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.556361Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b407913f-067d-4bb8-8d91-1324e0ef9f71 · outbound
This paper cites Pid accelerated value iter- ation algorithm.
Mathematical methods of reinforcement learning Pid accelerated value iter- ation algorithm
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.508074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2c8bdf71-bd6f-4350-9a2a-e9b4219b939b · outbound
This paper cites A unified view of entropy-regularized Markov decision processes.
Mathematical methods of reinforcement learning A unified view of entropy-regularized Markov decision processes
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T22:56:37.741188Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 09e0cabb-2143-49bd-98f3-a877dfec2952 · outbound
This paper cites Generative adversarial imitation learning.Advances in neural information processing systems, 29, 2016.
Mathematical methods of reinforcement learning Generative adversarial imitation learning.Advances in neural information processing systems, 29, 2016
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.603531Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f7070b96-34e2-494c-b0b0-d1c1feeab671 · outbound
This paper cites Variational policy gradient method for reinforcement learning with general utilities.
Mathematical methods of reinforcement learning Variational policy gradient method for reinforcement learning with general utilities
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.624435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5622de66-6cb8-415f-97d0-f0ce8f0e848a · outbound
This paper cites Stochastic Optimization under Hidden Convexity.
Mathematical methods of reinforcement learning Stochastic Optimization under Hidden Convexity
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T22:56:37.748081Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation dbe4491c-7c82-4134-b8e0-b2170fed9987 · outbound
This paper cites Model-based reinforcement learning with a generative model is minimax optimal.
Mathematical methods of reinforcement learning Model-based reinforcement learning with a generative model is minimax optimal
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.566394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b3016a84-d9d8-4c85-a4cb-4843e363023c · outbound
This paper cites Minimax pac bounds on the sample complexity of reinforcement learning with a generative model.
Mathematical methods of reinforcement learning Minimax pac bounds on the sample complexity of reinforcement learning with a generative model
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.584620Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1a4aa522-d10d-4def-85f3-f438933b7681 · outbound
This paper cites Breaking the sample size barrier in model-based reinforcement learning with a generative model.Advances in neural information processing systems, 33:12861–12872, 2020.
Mathematical methods of reinforcement learning Breaking the sample size barrier in model-based reinforcement learning with a generative model.Advances in neural information processing systems, 33:12861–12872, 2020
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.626147Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 54d16de7-e98c-48af-a929-5d175ed29e32 · outbound
This paper cites Near-optimal time and sample complexities for solving markov decision processes with a generative model.Advances in Neural Information Processing Systems, 31, 2018.
Mathematical methods of reinforcement learning Near-optimal time and sample complexities for solving markov decision processes with a generative model.Advances in Neural Information Processing Systems, 31, 2018
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.634321Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6be2a1cc-dc5f-4921-b98d-dd3ec061397b · outbound
This paper cites Reinforcement learning: Theory and algorithms.CS Dept., UW Seattle, Seattle, WA, USA, Tech.
Mathematical methods of reinforcement learning Reinforcement learning: Theory and algorithms.CS Dept., UW Seattle, Seattle, WA, USA, Tech
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.513427Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 96cf21f2-ea97-4172-a02d-64d3893fa985 · outbound
This paper cites Optimal sample complexity for average reward markov decision processes.
Mathematical methods of reinforcement learning Optimal sample complexity for average reward markov decision processes
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.544080Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0487e0b8-1526-48f8-9c37-f4a48152808e · outbound
This paper cites Sample complexity of asynchronous q-learning: Sharper analysis and variance reduction.Advances in neural information processing systems, 33:7031–7043, 2020.
Mathematical methods of reinforcement learning Sample complexity of asynchronous q-learning: Sharper analysis and variance reduction.Advances in neural information processing systems, 33:7031–7043, 2020
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.522052Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4493884f-f773-4eba-832e-1c6fd0507e4b · outbound
This paper cites From dirichlet to rubin: Optimistic exploration in rl without bonuses.
Mathematical methods of reinforcement learning From dirichlet to rubin: Optimistic exploration in rl without bonuses
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.532574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d468340e-52af-41dd-b1dd-b2e2251eb0b1 · outbound
This paper cites Pac bounds for discounted mdps.
Mathematical methods of reinforcement learning Pac bounds for discounted mdps
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.540846Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 03ef91a2-a2c4-4a05-be39-4e6be5e7af6d · outbound
This paper cites Assouad, fano, and le cam.
Mathematical methods of reinforcement learning Assouad, fano, and le cam
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.629352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation df529439-d5cf-4154-b3bb-2420c9b74978 · outbound
This paper cites Towards tight bounds on the sample complexity of average-reward mdps.
Mathematical methods of reinforcement learning Towards tight bounds on the sample complexity of average-reward mdps
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.558089Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 43a93845-8091-4c44-b98a-ccbbf5f8f77a · outbound
This paper cites pages 17, 18.
Mathematical methods of reinforcement learning pages 17, 18
Reference 31
- Resolution
- parse uncertain
- raw_fallback, observed 2026-07-09T23:06:37.535734Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e3272491-4db6-4eea-8e57-369a0a88a7b1 · outbound
This paper cites Freedman’s inequality for matrix martingales.
Mathematical methods of reinforcement learning Freedman’s inequality for matrix martingales
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.499104Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 14c9d5ae-ad9d-41aa-a718-6799b0080122 · outbound
This paper cites Is q-learning minimax optimal? a tight sample complexity analysis.Operations Research, 72(1): 222–236, 2024.
Mathematical methods of reinforcement learning Is q-learning minimax optimal? a tight sample complexity analysis.Operations Research, 72(1): 222–236, 2024
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.517104Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ce3c1e5a-e422-4beb-b2a8-c467b44733cf · outbound
This paper cites Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning.
Mathematical methods of reinforcement learning Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning
Reference 34
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T22:56:37.755639Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4c92c3c3-4836-42e6-9c2c-7c45aecd9f16 · outbound
This paper cites Finite-sample convergence rates for q-learning and indirect algorithms.Advances in neural information processing systems, 11, 1998.
Mathematical methods of reinforcement learning Finite-sample convergence rates for q-learning and indirect algorithms.Advances in neural information processing systems, 11, 1998
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.529072Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e0b3c3f8-23c4-41e9-8d6d-655bff861b99 · outbound
This paper cites A statistical analysis of polyak-ruppert averaged q-learning.
Mathematical methods of reinforcement learning A statistical analysis of polyak-ruppert averaged q-learning
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.500740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9f6c1b55-1e44-4b51-b1d4-082933adde0a · outbound
This paper cites Variance-reduced $Q$-learning is minimax optimal.
Mathematical methods of reinforcement learning Variance-reduced $Q$-learning is minimax optimal
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T22:56:37.736273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation feb90f15-4dac-4b57-bfa7-c55160ea031b · outbound
This paper cites Randomized linear programming solves the markov decision problem in nearly linear (sometimes sublinear) time.Mathematics of Operations Research, 45 (2):517–546, 2020.
Mathematical methods of reinforcement learning Randomized linear programming solves the markov decision problem in nearly linear (sometimes sublinear) time.Mathematics of Operations Research, 45 (2):517–546, 2020
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.504323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2e0b7dab-3863-4abd-9700-f2acaced553d · outbound
This paper cites Efficiently solving mdps with stochastic mirror descent.
Mathematical methods of reinforcement learning Efficiently solving mdps with stochastic mirror descent
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T22:56:37.502992Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation bd3b8edd-bdc9-461a-9a3d-e6a4e7b5c7e3 · outbound
This paper cites Solving matrix games with near-optimal matvec complexity.arXiv e-prints, pages arXiv–2601, 2026.
Mathematical methods of reinforcement learning Solving matrix games with near-optimal matvec complexity.arXiv e-prints, pages arXiv–2601, 2026
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.605225Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fec0dc65-3bd8-4650-b840-7325450d65b3 · outbound
This paper cites Minimumcostflows, mdps, andℓ1-regressioninnearlylinear time for dense instances.
Mathematical methods of reinforcement learning Minimumcostflows, mdps, andℓ1-regressioninnearlylinear time for dense instances
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.577819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6ad6faa2-2f65-49c6-8c8c-e0f01027688e · outbound
This paper cites Efficient global planning in large mdps via stochas- tic primal-dual optimization.
Mathematical methods of reinforcement learning Efficient global planning in large mdps via stochas- tic primal-dual optimization
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.579458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d98744b4-1240-4078-a40c-33ffb1653502 · outbound
This paper cites Tight high probability bounds for linear stochastic approximation with fixed stepsize.Advances in Neural Information Processing Systems, 34:30063–30074,.
Mathematical methods of reinforcement learning Tight high probability bounds for linear stochastic approximation with fixed stepsize.Advances in Neural Information Processing Systems, 34:30063–30074,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.617443Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d9a65bc3-ddaf-4f36-9980-b3134b9e1a5e · outbound
This paper cites Statistical inferenceforlinearstochasticapproximationwithmarkoviannoise.Advances in Neural Information Processing Systems, 38:174565–174626, 2026.
Mathematical methods of reinforcement learning Statistical inferenceforlinearstochasticapproximationwithmarkoviannoise.Advances in Neural Information Processing Systems, 38:174565–174626, 2026
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.572970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 863847bc-a59a-4fd4-8e9a-07ad930a98f9 · outbound
This paper cites Finite-time high-probability bounds for polyak–ruppert averaged iterates of linear stochastic ap- proximation.Mathematics of Operations Research, 50(2):935–964, 2025.
Mathematical methods of reinforcement learning Finite-time high-probability bounds for polyak–ruppert averaged iterates of linear stochastic ap- proximation.Mathematics of Operations Research, 50(2):935–964, 2025
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.527414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 02e7238c-bee3-44b2-9004-eb6d39afb31a · outbound
This paper cites Learning to predict by the methods of temporal differences.
Mathematical methods of reinforcement learning Learning to predict by the methods of temporal differences
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.636237Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation bfb97434-0e1c-4dd3-a150-90249a079648 · outbound
This paper cites Improved high-probability bounds for the temporal difference learning algorithm via exponential stability.
Mathematical methods of reinforcement learning Improved high-probability bounds for the temporal difference learning algorithm via exponential stability
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.627821Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0a4bfb33-aabc-4e9c-9539-b06517c8d8f6 · outbound
This paper cites pages 28.
Reference 48
- Resolution
- parse uncertain
- raw_fallback, observed 2026-07-09T23:06:37.632533Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ef7e5892-8397-48f7-8764-622c5dcfcec8 · outbound
This paper cites Residual algorithms: Reinforcement learning with function approx- imation.
Mathematical methods of reinforcement learning Residual algorithms: Reinforcement learning with function approx- imation
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.613814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a5451273-2d1f-47dd-9455-1dbcb9cc0c3a · outbound
This paper cites A convergento(n)temporal- difference algorithm for off-policy learning with linear function approximation.Ad- vances in neural information processing systems, 21, 2008.
Mathematical methods of reinforcement learning A convergento(n)temporal- difference algorithm for off-policy learning with linear function approximation.Ad- vances in neural information processing systems, 21, 2008
Reference 50
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- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.559853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 58568cbb-93ce-4db7-81e0-575fa443bc02 · outbound
This paper cites S Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, and E.
Mathematical methods of reinforcement learning S Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, and E
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.588139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c87c284a-cceb-44e7-8b02-e9540456c859 · outbound
This paper cites Gaussian approximation for two-timescale linear stochastic approximation.
Mathematical methods of reinforcement learning Gaussian approximation for two-timescale linear stochastic approximation
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.563024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e41f464a-319b-49fc-a105-45a192fac53e · outbound
This paper cites Some aspects of the sequential design of experiments.Bulletin of the American Mathematical Society, 58(5):527–535, 1952.
Mathematical methods of reinforcement learning Some aspects of the sequential design of experiments.Bulletin of the American Mathematical Society, 58(5):527–535, 1952
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.586393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7b959147-979c-4d20-9f6a-c3687757c0bf · outbound
This paper cites Introduction to multi-armed bandits.Foundations and Trends in Machine Learning, 12(1-2):1–286, 2019.
Mathematical methods of reinforcement learning Introduction to multi-armed bandits.Foundations and Trends in Machine Learning, 12(1-2):1–286, 2019
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.590019Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 23df21be-6c13-4283-ac22-b146bde277c6 · outbound
This paper cites Regret analysis of stochastic and non- stochastic multi-armed bandit problems.Foundations and Trends in Machine Learn- ing, 5(1):1–122, 2012.
Mathematical methods of reinforcement learning Regret analysis of stochastic and non- stochastic multi-armed bandit problems.Foundations and Trends in Machine Learn- ing, 5(1):1–122, 2012
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.598532Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 47bc7bf8-28c9-4c32-9409-32c93948d2a8 · outbound
This paper cites Asymptoticallyefficientadaptiveallocationrules.
Mathematical methods of reinforcement learning Asymptoticallyefficientadaptiveallocationrules
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.610463Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2a30c6b4-4d98-45f2-a1d0-21d84d106c8e · outbound
This paper cites Finite-time analysis of the multi- armed bandit problem.Machine Learning, 47(2-3):235–256, 2002.
Mathematical methods of reinforcement learning Finite-time analysis of the multi- armed bandit problem.Machine Learning, 47(2-3):235–256, 2002
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.630993Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e6d3077f-f3c9-49ff-a166-00df0f9538d1 · outbound
This paper cites Onthelikelihoodthatoneunknownprobabilityexceedsanother in view of the evidence of two samples.Biometrika, 25(3/4):285–294, 1933.
Mathematical methods of reinforcement learning Onthelikelihoodthatoneunknownprobabilityexceedsanother in view of the evidence of two samples.Biometrika, 25(3/4):285–294, 1933
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.571273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 29f09b7d-f6e8-4f16-b3d6-9a49501a13c5 · outbound
This paper cites A tutorial on thompson sampling.Foundations and Trends®in Machine Learning, 11(1):1–96,.
Mathematical methods of reinforcement learning A tutorial on thompson sampling.Foundations and Trends®in Machine Learning, 11(1):1–96,
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.582894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e2f8921d-b4fd-4696-be06-b55b748311c8 · outbound
This paper cites Cambridge University Press,.
Mathematical methods of reinforcement learning Cambridge University Press,
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.568008Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation efca1d98-2947-4f77-9b0e-b9d756863152 · outbound
This paper cites An empirical evaluation of thompson sampling.Ad- vances in neural information processing systems, 24, 2011.
Mathematical methods of reinforcement learning An empirical evaluation of thompson sampling.Ad- vances in neural information processing systems, 24, 2011
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.596758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d423b61c-bdb3-4f7e-94ee-efa0dbf881b9 · outbound
This paper cites Further optimal regret bounds for thompson sam- pling.
Mathematical methods of reinforcement learning Further optimal regret bounds for thompson sam- pling
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.574497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3c3b4746-6292-457a-92ed-0c14e1feec65 · outbound
This paper cites Analysis of thompson sampling for the multi-armed bandit problem.
Mathematical methods of reinforcement learning Analysis of thompson sampling for the multi-armed bandit problem
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.552759Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 38ea0d67-7489-4b00-a8eb-0af066c4cb07 · outbound
This paper cites Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems.
Mathematical methods of reinforcement learning Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
Reference 64
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-09T22:56:37.733876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a2a1c426-57ee-474a-b6ba-4300d2ed6a4e · outbound
This paper cites Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited.
Mathematical methods of reinforcement learning Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.554424Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 979c3d86-060e-44e1-af69-b9d76768720a · outbound
This paper cites Near-optimal regret bounds for reinforcement learning.Journal of Machine Learning Research, 11:1563–1600, 2010.
Mathematical methods of reinforcement learning Near-optimal regret bounds for reinforcement learning.Journal of Machine Learning Research, 11:1563–1600, 2010
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.591710Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 658b9dd7-2d3d-4471-b1b6-bfacffd5ecc0 · outbound
This paper cites A unifying view of optimism in episodic reinforce- ment learning.
Mathematical methods of reinforcement learning A unifying view of optimism in episodic reinforce- ment learning
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.612156Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f807cfed-f852-4d1f-b30b-0ed459468af8 · outbound
This paper cites Minimax regret bounds for reinforcement learning.
Mathematical methods of reinforcement learning Minimax regret bounds for reinforcement learning
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.606967Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 52656bc2-427d-4740-8e89-23bada35e115 · outbound
This paper cites Rusu, Joel Veness, Marc G.
Mathematical methods of reinforcement learning Rusu, Joel Veness, Marc G
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.561434Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f5001757-cc5a-44e4-83b6-102cd1caed61 · outbound
This paper cites Asynchronous methods for deep reinforcement learning.
Mathematical methods of reinforcement learning Asynchronous methods for deep reinforcement learning
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.593511Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2bc3cfb2-7dcf-4522-92ec-8d4245b58e78 · outbound
This paper cites Trust region policy optimization.
Mathematical methods of reinforcement learning Trust region policy optimization
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.595136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fb2e76d8-792e-4984-bf33-350c04862815 · outbound
This paper cites Generalization and exploration via randomized value functions.
Mathematical methods of reinforcement learning Generalization and exploration via randomized value functions
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T22:56:37.508406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5f7bc0a9-979f-42ac-b971-81f27e2b1922 · outbound
This paper cites Is q-learning provably efficient?Advances in neural information processing systems, 31, 2018.
Mathematical methods of reinforcement learning Is q-learning provably efficient?Advances in neural information processing systems, 31, 2018
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.576159Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 62b6891e-6bcc-4c72-84e4-ea8c406cacd2 · outbound
This paper cites Almost optimal model-free reinforce- ment learningvia reference-advantage decomposition.Advances in Neural Information Processing Systems, 33:15198–15207, 2020.
Mathematical methods of reinforcement learning Almost optimal model-free reinforce- ment learningvia reference-advantage decomposition.Advances in Neural Information Processing Systems, 33:15198–15207, 2020
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.581204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4c2dc31f-1b9f-47a1-9fd4-518414f1f8ca · outbound
This paper cites (more) efficient reinforcement learning via posterior sampling.Advances in Neural Information Processing Systems, 26, 2013.
Mathematical methods of reinforcement learning (more) efficient reinforcement learning via posterior sampling.Advances in Neural Information Processing Systems, 26, 2013
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.564706Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 43600ac8-cd84-486c-b1a9-c8c9a9d5e4ae · outbound
This paper cites Optimistic posterior sampling for reinforcement learn- ing: Worst-caseregretbounds.
Mathematical methods of reinforcement learning Optimistic posterior sampling for reinforcement learn- ing: Worst-caseregretbounds
Reference 76
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.622702Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b1a56240-a299-4146-b4a0-0f903b189cb8 · outbound
This paper cites Optimistic pos- terior sampling for reinforcement learning with few samples and tight guarantees.
Mathematical methods of reinforcement learning Optimistic pos- terior sampling for reinforcement learning with few samples and tight guarantees
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.518797Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a52d8616-c9df-447f-85d0-ccdeea1502c1 · outbound
This paper cites Deep exploration via randomized value functions.Journal of machine learning research, 20(124):1–62,.
Mathematical methods of reinforcement learning Deep exploration via randomized value functions.Journal of machine learning research, 20(124):1–62,
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- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.537575Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c5f613ff-65f0-4b19-abbf-5344212064b4 · outbound
This paper cites Worst-case regret bounds for exploration via randomized value func- tions.Advances in neural information processing systems, 32, 2019.
Mathematical methods of reinforcement learning Worst-case regret bounds for exploration via randomized value func- tions.Advances in neural information processing systems, 32, 2019
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.525461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ee221d4e-9358-46e7-8e9b-ff9a6167f679 · outbound
This paper cites Improved worst-case regret bounds for randomized least-squares value iteration.
Mathematical methods of reinforcement learning Improved worst-case regret bounds for randomized least-squares value iteration
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.523757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8b7ce566-a3cb-43f5-b7ab-a007538120c3 · outbound
This paper cites Near-optimal randomized exploration for tabular markov decision processes.Advances in neural information processing systems, 35:6358–6371, 2022.
Mathematical methods of reinforcement learning Near-optimal randomized exploration for tabular markov decision processes.Advances in neural information processing systems, 35:6358–6371, 2022
Reference 81
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.619182Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fd5a70ac-0eba-40e5-a724-ef79cb96a95a · outbound
This paper cites Finite-time bounds for fitted value iteration.
Mathematical methods of reinforcement learning Finite-time bounds for fitted value iteration
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.545725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4d41807c-a370-4f7a-8279-977d1e35412e · outbound
This paper cites Error bounds for approximate policy iteration.
Mathematical methods of reinforcement learning Error bounds for approximate policy iteration
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.539240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation cecc9e32-113b-4da4-9e6b-c1bc17acc3e6 · outbound
This paper cites Kernel-based reinforcement learning.Machine learn- ing, 49(2):161–178, 2002.
Mathematical methods of reinforcement learning Kernel-based reinforcement learning.Machine learn- ing, 49(2):161–178, 2002
Reference 84
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.608890Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8cda6af4-4335-461f-a18d-36c3d25cfcdd · outbound
This paper cites Kernel-based reinforcement learning: A finite-time analysis.
Mathematical methods of reinforcement learning Kernel-based reinforcement learning: A finite-time analysis
Reference 85
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.506146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 99c9189c-d339-4b0c-8a72-91fb4b45f762 · outbound
This paper cites Adaptive discretization for episodic reinforcement learning in metric spaces.Proceedings of the ACM on Measurement and Analysis of Computing Systems, 3(3):1–44, 2019.
Mathematical methods of reinforcement learning Adaptive discretization for episodic reinforcement learning in metric spaces.Proceedings of the ACM on Measurement and Analysis of Computing Systems, 3(3):1–44, 2019
Reference 86
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T22:56:38.249924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d0d4ad1a-f395-4342-a425-e3f90a0f01a5 · outbound
This paper cites Provably efficient reinforcement learning with linear function approximation.
Mathematical methods of reinforcement learning Provably efficient reinforcement learning with linear function approximation
Reference 87
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T22:56:38.247742Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c462ef11-47a3-4f1e-bd2c-9c342e4f963a · outbound
This paper cites Linear least-squares algorithms for temporal difference learning.Machine learning, 22(1):33–57, 1996.
Mathematical methods of reinforcement learning Linear least-squares algorithms for temporal difference learning.Machine learning, 22(1):33–57, 1996
Reference 88
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T22:56:38.251963Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2d142eb7-5900-4cab-b928-4404be97753c · outbound
This paper cites Reinforcement learning of motor skills with policy gradients.Neural Networks, 21:682–697, 2008.
Mathematical methods of reinforcement learning Reinforcement learning of motor skills with policy gradients.Neural Networks, 21:682–697, 2008
Reference 89
- Resolution
- verified exact
- doi, observed 2026-07-09T22:56:37.583878Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 221dcbd7-c929-41e7-828b-9211b9e2bb69 · outbound
This paper cites Williams.
Reference 90
- Resolution
- malformed identifier
- raw_fallback, observed 2026-07-09T22:56:38.244791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 326af9d8-7ac9-4992-bebb-6aece3e97238 · outbound
This paper cites Konda and John N.
Mathematical methods of reinforcement learning Konda and John N
Reference 91
- Resolution
- verified exact
- doi, observed 2026-07-09T22:56:37.595607Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 808f8a33-eef2-446c-9f53-1c5fda42994a · outbound
This paper cites Neurocomputing , volume =.
Mathematical methods of reinforcement learning Neurocomputing , volume =
Reference 92
- Resolution
- verified exact
- doi, observed 2026-07-09T22:56:37.579375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5672a2f2-c10d-4a5d-bd8f-30cc7aa3bdeb · outbound
This paper cites Arulkumaran, M.
Mathematical methods of reinforcement learning Arulkumaran, M
Reference 93
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-09T22:56:37.588235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 24823e60-4c89-4f5a-b96b-bdefb12188c6 · outbound
This paper cites Trust region policy opti- mization via entropy regularization for Kullback–Leibler divergence constraint.Neu- rocomputing, 589:127716, 2024.
Mathematical methods of reinforcement learning Trust region policy opti- mization via entropy regularization for Kullback–Leibler divergence constraint.Neu- rocomputing, 589:127716, 2024
Reference 94
- Resolution
- verified exact
- arxiv_id, observed 2026-07-09T22:56:37.582084Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c501ec6a-0c56-43ba-b1f9-526212f30e55 · outbound
This paper cites Policy mirror descent for reinforcement learning: Linear convergence, new sampling complexity, and generalized problem classes.Mathematical Program- ming, 198(1):1059–1106, 2023.
Mathematical methods of reinforcement learning Policy mirror descent for reinforcement learning: Linear convergence, new sampling complexity, and generalized problem classes.Mathematical Program- ming, 198(1):1059–1106, 2023
Reference 95
- Resolution
- verified exact
- doi, observed 2026-07-09T22:56:37.585701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d760a2a1-0f15-48a9-ad3c-69d5e7d59b67 · outbound
This paper cites Proximal Policy Optimization Algorithms.
Mathematical methods of reinforcement learning Proximal Policy Optimization Algorithms
Reference 96
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T22:56:37.772074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 979c69d2-4ca7-4ef3-b852-00ca17d33eeb · outbound
This paper cites Improving proximal policy optimization with alpha divergence.Neurocomputing, 534:94–105,.
Mathematical methods of reinforcement learning Improving proximal policy optimization with alpha divergence.Neurocomputing, 534:94–105,
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- verified fuzzy
- raw_fallback, observed 2026-07-09T23:06:37.551094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4e4ac8e6-8628-4fe0-ad4a-87600a2dfaae · outbound
This paper cites pages 54.
Reference 98
- Resolution
- verified exact
- doi, observed 2026-07-09T22:56:37.597170Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 89263e8a-a2e3-4f87-ae49-77870af71df6 · outbound
This paper cites Reinforcement Learning from Human Feedback: A Statistical Perspective.
Mathematical methods of reinforcement learning Reinforcement Learning from Human Feedback: A Statistical Perspective
Reference 99
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T22:56:37.791390Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 110d358f-45d6-4fc6-a555-e67f35b831ad · outbound
This paper cites When Do Off-Policy and On-Policy Policy Gradient Methods Align?.
Mathematical methods of reinforcement learning When Do Off-Policy and On-Policy Policy Gradient Methods Align?
Reference 100
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T22:56:37.784291Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.