Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-22T21:35:25.674483Z
Paper Citation Record · LEDGER
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control
As of 28 July 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2504.02710.
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 2026-07-28T06:31:03.373048+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-10T19:11:06.626334Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T19:17:31.580287Z
Reference resolution
23 of 23 outbound references displayed
- verified exact4
- verified fuzzy19
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation a14395c0-1210-4a40-9477-bdb706722b27 · outbound
This paper cites Proximal Policy Optimization Algorithms.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Proximal Policy Optimization Algorithms
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T21:37:09.040561Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 20beb890-4dba-4350-9f2f-f7a2351dc6eb · outbound
This paper cites an unresolved cited work.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Unresolved cited work
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.422050Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 857d1162-3c7b-4b87-baf2-a152826f638e · outbound
This paper cites Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T21:37:09.015040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 2442ed26-4481-42b9-ae7f-14b6ca9053c3 · outbound
This paper cites AC4MPC: Actor-Critic Reinforcement Learning for Nonlinear Model Predictive Control.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control AC4MPC: Actor-Critic Reinforcement Learning for Nonlinear Model Predictive Control
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T21:37:09.024479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 54cf89d3-1c3b-4255-9ca1-81bd681e91bb · outbound
This paper cites Convex neural network-based cost modifications for learning model predictive control.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Convex neural network-based cost modifications for learning model predictive control
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.452773Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 91a583ce-1275-4c5a-a4e6-07b03a9ec38e · outbound
This paper cites Learning Lyapunov terminal costs from data for complexity reduction in nonlinear model predictive control.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Learning Lyapunov terminal costs from data for complexity reduction in nonlinear model predictive control
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.396007Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation ca7d50be-cbd0-4b84-9f93-d45771e419f2 · outbound
This paper cites Stabilizing receding- horizon control of nonlinear time varying systems.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Stabilizing receding- horizon control of nonlinear time varying systems
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.402500Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation a9fd4a91-926c-4125-9802-407cc4b939dc · outbound
This paper cites A stabilizing model-based predictive control for nonlinear systems.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control A stabilizing model-based predictive control for nonlinear systems
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.456206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation e26b0224-21b3-4cb7-a434-e5a15419af31 · outbound
This paper cites Efficient NMPC of unsta- ble periodic systems using approximate infinite horizon closed loop costing.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Efficient NMPC of unsta- ble periodic systems using approximate infinite horizon closed loop costing
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.449775Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 145a2e17-f235-4a3d-8ed4-1700317ad8ae · outbound
This paper cites Bertsekas and J.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Bertsekas and J
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.409375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation f0c6e5ec-fcbf-46d2-8290-07664291a7b9 · outbound
This paper cites Multi-phase optimal control problems for efficient nonlinear model predictive control with acados.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Multi-phase optimal control problems for efficient nonlinear model predictive control with acados
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.446450Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation fef5fed1-4773-4976-bea6-4e7bc6ff11e5 · outbound
This paper cites A partially tightened real-time iteration scheme for nonlinear model predictive control.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control A partially tightened real-time iteration scheme for nonlinear model predictive control
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.442792Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 8354239d-fc8a-41d1-a23a-8e0efe70fb22 · outbound
This paper cites Inexact methods for nonlinear model predictive control: stability, applications, and software.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Inexact methods for nonlinear model predictive control: stability, applications, and software
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.432502Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 7eb4dd9d-c06d-4a9a-8b31-054e5daf4860 · outbound
This paper cites Stability analysis of nonlinear model predictive control with progressive tightening of stage costs and constraints.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Stability analysis of nonlinear model predictive control with progressive tightening of stage costs and constraints
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.439325Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation bd3ce14a-9bd7-4e94-a2d0-56c932e0b047 · outbound
This paper cites A Lyapunov function for the combined system-optimizer dynamics in inexact model predictive control.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control A Lyapunov function for the combined system-optimizer dynamics in inexact model predictive control
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.406057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 64bbde95-07f3-4a63-9f46-f74217578b14 · outbound
This paper cites A real-time iteration scheme for nonlinear optimization in optimal feedback control.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control A real-time iteration scheme for nonlinear optimization in optimal feedback control
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.428916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 79b599b1-4533-4f01-b11d-7bd9a8f62fc5 · outbound
This paper cites Nocedal and S.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Nocedal and S
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.425419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 6db20fbe-6fe3-4658-ab65-7c612c9aa85c · outbound
This paper cites The lifted Newton method and its application in optimization.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control The lifted Newton method and its application in optimization
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.419124Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation e1695ff7-d441-4f7a-b3d1-7cbf71ad76d3 · outbound
This paper cites Safe-control-gym: A unified benchmark suite for safe learning-based control and reinforcement learning in robotics.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Safe-control-gym: A unified benchmark suite for safe learning-based control and reinforcement learning in robotics
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.399426Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 85e6611f-0c60-421e-af96-5f00cccfed5e · outbound
This paper cites acados – a modular open-source framework for fast embedded optimal control.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control acados – a modular open-source framework for fast embedded optimal control
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.412772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 213893ce-4ad4-4982-ad05-fac8d71e87ff · outbound
This paper cites HPIPM: a high-performance quadratic programming framework for model predictive control.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control HPIPM: a high-performance quadratic programming framework for model predictive control
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.435643Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation f70dc191-9f67-46dd-a00d-d0b7bc468579 · outbound
This paper cites Design of a Trajectory Tracking Controller for a Nanoquadcopter.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Design of a Trajectory Tracking Controller for a Nanoquadcopter
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T21:37:09.031186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Observation 478d04dd-195d-41e3-8416-57e446bfc139 · outbound
This paper cites Learning for casadi: Data-driven models in numerical optimization.
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control Learning for casadi: Data-driven models in numerical optimization
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T21:37:09.416132Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.
Pith citing papers
Observation 834c539c-d8ef-479f-8d63-ae70da549551 · inbound
Improving greenhouse fruit-production control by integrating reinforcement learning into short-horizon model predictive control cites this paper.
Improving greenhouse fruit-production control by integrating reinforcement learning into short-horizon model predictive control Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control
Reference 9
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-10T19:17:31.582034Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.