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Paper Citation Record · LEDGER

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction

As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2507.22640.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.22640 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:34:23.413516Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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  • verified fuzzy18
  • unresolved20
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 47d0afb2-e201-4c9c-91b8-555d6cd64d96 · outbound

This paper cites Reinforcement Learning: An Introduction,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Reinforcement Learning: An Introduction,

Reference 1

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 774554ce-b087-4f03-8bc8-5edd85b24f8c · outbound

This paper cites From automated to autonomous process operations,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction From automated to autonomous process operations,

Reference 2

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:17.019529Z digest=sha256:77ff2024aeb5272333cba8bb6b8e1243e0dfe36ae7402e034d4e1c225453d943

Observation 1c1f6956-1bd8-4db8-b9ec-41dfddbcc373 · outbound

This paper cites Concrete Problems in AI Safety.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Concrete Problems in AI Safety

Reference 3

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Observation 508c8ec1-2f66-422f-a823-a991a2c02b1b · outbound

This paper cites Optimal grade transition for polyethylene reactors via NCO tracking,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Optimal grade transition for polyethylene reactors via NCO tracking,

Reference 4

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c5c74b45-b2e3-4584-8979-918730fc3169 · outbound

This paper cites Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables,

Reference 5

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:17.502796Z digest=sha256:0cb1c9074e59da0dcc783eb66970117d9a8dd8fc77adadf7579edc6adef56091

Observation ec392a86-50b6-4a89-922f-37f1aa9bb745 · outbound

This paper cites Integrated scheduling and dynamic optimization of grade transitions for a continuous polymerization reactor,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Integrated scheduling and dynamic optimization of grade transitions for a continuous polymerization reactor,

Reference 6

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Observation 99ea38c8-7d60-44dc-b15a-e78182ec8bf7 · outbound

This paper cites The general problem of the stability of motion,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction The general problem of the stability of motion,

Reference 7

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:17.887751Z digest=sha256:8e5fb7761fd2d276df5894172c199102a151601e61f51a88def5f4be5f6354a3

Observation 37dbd767-c63b-4c32-a7f4-cfbedc629599 · outbound

This paper cites an unresolved cited work.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-06T11:34:18.102389Z digest=sha256:cbab854879c6cad27a9fd34b2ca94f0f9b664fa0c2c3d330bb13a015437fd921

Observation eb5cd566-8ea5-4177-b7a8-d22666238a99 · outbound

This paper cites Input Convex Neural Networks.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Input Convex Neural Networks

Reference 9

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source=pdf_text observed=2026-08-06T11:34:18.321318Z digest=sha256:6b494eaac00854925a27d1ecbb362bb8a65872054fdc9560a499ec3bd42406d0

Observation aeeaa0b7-fca2-4bff-938c-093e73a7bd10 · outbound

This paper cites Safe Model-based Reinforcement Learning with Stability Guarantees.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Safe Model-based Reinforcement Learning with Stability Guarantees

Reference 10

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source=pdf_text observed=2026-08-06T11:34:18.448416Z digest=sha256:4ae9b5a69ae295a150c31fc65c4e03348f7ae0088cac1f173169d19094b31d05

Observation dc70046b-ce11-4f08-afec-de8e3b61fd2f · outbound

This paper cites Control Barrier Function Based Quadratic Pro- grams for Safety Critical Systems,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Control Barrier Function Based Quadratic Pro- grams for Safety Critical Systems,

Reference 11

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source=pdf_text observed=2026-08-06T11:34:18.607406Z digest=sha256:f4637791e3b4ad9c541f64f70dc8ca0ada0df74a72fd86f50e24044a4d50ac25

Observation a584b792-8643-44bb-8e73-9c5b521064e9 · outbound

This paper cites Safe and Stable RL (S2RL) Driving Policies Using Control Barrier and Control Lyapunov Functions,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Safe and Stable RL (S2RL) Driving Policies Using Control Barrier and Control Lyapunov Functions,

Reference 12

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:18.749875Z digest=sha256:97f7783314e398271d36a21d087c6423c3422a1132eff110e4e9e5ab6e42eea0

Observation 6fbb22bf-b6ad-4ad6-a4a7-bd1ad960f3de · outbound

This paper cites Constrained Policy Optimization.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Constrained Policy Optimization

Reference 13

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source=pdf_text observed=2026-08-06T11:34:18.893392Z digest=sha256:baa62895bee74e5d19c7c15d1ca948b3f1b16541d58bc23d5cbf721a4b673f32

Observation 3d6583d2-0b5b-4715-98a1-3576e07c9e16 · outbound

This paper cites Safe Exploration in Continuous Action Spaces.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Safe Exploration in Continuous Action Spaces

Reference 14

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source=pdf_text observed=2026-08-06T11:34:19.104125Z digest=sha256:d39794dfbac46e9c399d915c6e6d47230cbf8f7a0e6e68dbeac54d22288dcb84

Observation 10db63f3-0676-4291-8c36-01bea85205dc · outbound

This paper cites Conservative Q-Learning for Offline Reinforcement Learning.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Conservative Q-Learning for Offline Reinforcement Learning

Reference 15

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source=pdf_text observed=2026-08-06T11:34:19.197355Z digest=sha256:7ba59f2437d9b3b1cc2c2f194fb9e10e0c6a57a7de9edabf16d59df7fad732da

Observation c007703d-cd76-4faa-97ee-60b7af00f21f · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Offline Reinforcement Learning with Implicit Q-Learning

Reference 16

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source=pdf_text observed=2026-08-06T11:34:19.396964Z digest=sha256:4dba6578b6addbe8d1acc602a73d91b205ca3f9139a32e70a23161fe8fd6439f

Observation cf93d61e-21ef-4c72-b842-e954f85b855f · outbound

This paper cites MOPO: Model-based Offline Policy Optimization.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction MOPO: Model-based Offline Policy Optimization

Reference 17

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source=pdf_text observed=2026-08-06T11:34:19.560544Z digest=sha256:dfc45353057c01eb1ddb1818b53a6019663fcf9cfb49bd5a1de7da50dcb9f0da

Observation 4ccfd0f9-726d-4af3-afb6-4699c5e56739 · outbound

This paper cites Actor–Critic Physics-Informed Neural Lyapunov Con- trol,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Actor–Critic Physics-Informed Neural Lyapunov Con- trol,

Reference 18

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:19.741550Z digest=sha256:284128355bbd62a719b2af344fc6186cf841ec647c110ee0c571f7f7f503423a

Observation 96778281-0ef6-4a6c-8dc0-c64a2fd2973b · outbound

This paper cites Distributional Reinforcement Learning with Quantile Regression.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Distributional Reinforcement Learning with Quantile Regression

Reference 19

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source=pdf_text observed=2026-08-06T11:34:19.925848Z digest=sha256:ed78975c657d7e7e25439f9b4020539862fddfceb229b09ba381368799ada8a6

Observation 370834ac-9400-41f1-a108-4603f8744f26 · outbound

This paper cites EKG-AC: A New Paradigm for Process Indus- trial Optimization Based on Offline Reinforcement Learning With Expert Knowledge Guidance,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction EKG-AC: A New Paradigm for Process Indus- trial Optimization Based on Offline Reinforcement Learning With Expert Knowledge Guidance,

Reference 20

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:20.077489Z digest=sha256:f807003043587a1bb8b728abdc5ec4c67bdc9a1bdb25e3fb374b14ad7ae05d4f

Observation 3826ad44-7d6d-4395-9796-201a044176c5 · outbound

This paper cites Optimal Control Via Neural Networks: A Convex Approach.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Optimal Control Via Neural Networks: A Convex Approach

Reference 21

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source=pdf_text observed=2026-08-06T11:34:20.306372Z digest=sha256:db5dabf40c6a9198cd4575bceeead478de91f94f83a9f8428a032675cc20553d

Observation feab1efd-3c40-4dfb-9e6a-13241dd895b7 · outbound

This paper cites Differentiable Convex Optimization Layers.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Differentiable Convex Optimization Layers

Reference 22

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source=pdf_text observed=2026-08-06T11:34:20.442610Z digest=sha256:8cb551d8feb2c085af3790f96fcb263d0f13e20bd6797394eb832a86c7e9276a

Observation cb8b85ae-3c1d-4330-9859-32c9be6729d1 · outbound

This paper cites OptNet: Differentiable Optimization as a Layer in Neural Networks,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction OptNet: Differentiable Optimization as a Layer in Neural Networks,

Reference 23

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Observation 8d8fd4b8-b759-4fe9-89ab-822f849d849d · outbound

This paper cites Polymer grade transition control using advanced real-time optimization software,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Polymer grade transition control using advanced real-time optimization software,

Reference 24

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation f69c0586-8e19-4c5e-bd61-6aebe21db289 · outbound

This paper cites Polymer grade transition control via reinforcement learning trained with a physically consistent memory sequence-to-sequence digital twin,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Polymer grade transition control via reinforcement learning trained with a physically consistent memory sequence-to-sequence digital twin,

Reference 25

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:21.154028Z digest=sha256:417aad4bc5c67ef015f88be88c329e8f6cf27ccfddfae395c1b349060afee1ba

Observation 0ba9b1af-a386-4ca0-b2af-30e290e13a68 · outbound

This paper cites A benchmark environment motivated by industrial control problems,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction A benchmark environment motivated by industrial control problems,

Reference 26

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:21.296216Z digest=sha256:694fa058503ba0df567d48e80e44c175c993091b8b03fbfa374c1328b43b6afb

Observation 9e3f99c7-1864-46b0-97c3-b30cd9b1c067 · outbound

This paper cites Benchmarking Batch Deep Reinforcement Learning Algorithms.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 27

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source=pdf_text observed=2026-08-06T11:34:21.442854Z digest=sha256:dd4f4eeae15521866c78c94138d6c0f93d381ed2e1849c209f0ec840cb94b253

Observation ef465ad6-a07f-4c36-8315-f2958d7f4a43 · outbound

This paper cites PC-Gym: Benchmark Environments For Process Control Problems.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction PC-Gym: Benchmark Environments For Process Control Problems

Reference 28

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source=pdf_text observed=2026-08-06T11:34:21.584194Z digest=sha256:2213834a2fb63de4cdcf37e69f14cf807007742ff139057b98feac6120ce0c0b

Observation eb487b2f-da8a-49e3-86a9-7867a95ee095 · outbound

This paper cites End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

Reference 29

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local_arxiv, observed 2026-08-06T11:34:23.746247Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:21.799762Z digest=sha256:02a7f116d0aea982d5cd41923b48719c7e8dd80440c5181342f61392a215694e

Observation 203fd197-31c7-47a0-99e1-e90cc81eff2c · outbound

This paper cites Offline reinforcement learning methods for real-world problems,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Offline reinforcement learning methods for real-world problems,

Reference 30

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:22.030643Z digest=sha256:257f3d8ac894a2cf912c11fd1cd8490766803aada7947fc4a4bb9b47040172bb

Observation dae50e37-7c3d-4137-9e10-976a79f04421 · outbound

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

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 31

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source=pdf_text observed=2026-08-06T11:34:22.256553Z digest=sha256:eb7a0862827ac33dbfab7299e637ed256cb64c4f5c783e2f8481102f0988c5af

Observation fe6784ac-7e2f-4dcc-b52e-8fa68cd782bf · outbound

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

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction A survey on offline reinforcement learning: Taxonomy, review, and open problems,

Reference 32

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source=pdf_text observed=2026-08-06T11:34:22.483538Z digest=sha256:ec39be053376558b2a89d7650b29e2e169c73bd715dbb8eadd5a18fa8203e2c8

Observation dce9edd8-9547-4c5a-8e76-9faad7a9eafd · outbound

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

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Stabilizing off-policy q-learning via bootstrapping error reduction,

Reference 33

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raw_fallback, observed 2026-08-06T11:34:25.474108Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:22.678861Z digest=sha256:268b5bf98ebfdf99e12f2018c35118798175a80f609edb63f72bd1bfe0ca5196

Observation bf7f5e39-0013-46cb-9550-a3bcbe6650ab · outbound

This paper cites Deep Reinforcement Learning with Double Q-learning.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Deep Reinforcement Learning with Double Q-learning

Reference 34

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source=pdf_text observed=2026-08-06T11:34:22.841058Z digest=sha256:62c1f8064e34055be9b37eacdc6e860a270ba277f4782580967343a4aaf05790

Observation f22b21aa-dcd3-4daa-a3f5-ab7a82bb7621 · outbound

This paper cites Human-level control through deep reinforcement learning,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Human-level control through deep reinforcement learning,

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T11:34:23.006757Z digest=sha256:a1bfe50bd18b05fa8b7fade4e0e1cf07875c0f23b977c939dbbe3bc2db1f8d17

Observation 2d55ea92-d8cd-437c-b857-b4e5247f3f56 · outbound

This paper cites Behavior Regularized Offline Reinforcement Learning.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Behavior Regularized Offline Reinforcement Learning

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 176e6c8e-b993-44a6-a594-cc0c91212bb0 · outbound

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

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 37

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source=pdf_text observed=2026-08-06T11:34:23.313489Z digest=sha256:1adc0a0b9c8bb18accf83cb9f216031f9a3fcc079df09255f934ab2a4e502d72

Observation 09431ac7-47f5-49c0-a3bc-9ec13e86c089 · outbound

This paper cites Comparative Study of Machine Learning and System Identification for Process Systems Engineering Dynamics,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Comparative Study of Machine Learning and System Identification for Process Systems Engineering Dynamics,

Reference 38

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Observation bc6fd994-6286-4aed-8703-3ba5690351f0 · outbound

This paper cites Polymerization reactor control using autoregressive-plus Volterra- based MPC,.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Polymerization reactor control using autoregressive-plus Volterra- based MPC,

Reference 39

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Observation 8ff2815d-9103-4710-be2f-c89055b741cc · outbound

This paper cites OptNet: Differentiable Optimization as a Layer in Neural Networks.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction OptNet: Differentiable Optimization as a Layer in Neural Networks

Reference 2021

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Pith citing papers

No inbound Pith citation observations are available.