Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2103.09568.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:24:35.939096Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T12:56:56.519041Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 8b001b4d-dbd5-4e9b-a3c7-5eb0d8d1b81d · inbound
CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Driving cites this paper.
CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Driving A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:24:35.939096Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f489e4db-de73-4c01-9abb-32c4f75de5d0 · inbound
A Single Deep Preference-Conditioned Policy for Learning Pareto Coverage Sets cites this paper.
A Single Deep Preference-Conditioned Policy for Learning Pareto Coverage Sets A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T07:41:42.437108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 9d5e7ae1-f2dc-4456-a981-868f1d048743 · inbound
Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization cites this paper.
Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T19:32:51.698497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 93544ef8-4a67-4a29-98e5-ab7f715ffcee · inbound
FlowCompile: An Optimizing Compiler for Structured LLM Workflows cites this paper.
FlowCompile: An Optimizing Compiler for Structured LLM Workflows A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T19:49:26.000370Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 396cce18-9a7e-4c2a-987f-3a89fb2efcce · inbound
A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems cites this paper.
A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T23:49:15.114217Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 9757f65f-f879-402a-933a-79df91a63ab2 · inbound
Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning cites this paper.
Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T13:05:44.748395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c51bc9e1-d664-4b5d-84d9-193d20158941 · inbound
AETDICE: Unified Framework and Offline Optimization for Nonlinear Multi-Objective RL cites this paper.
AETDICE: Unified Framework and Offline Optimization for Nonlinear Multi-Objective RL A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T09:05:37.365422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation fcaed741-1456-4d89-81ab-a6ecf12c379d · inbound
Coachable agents for interactive gameplay cites this paper.
Coachable agents for interactive gameplay A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T12:56:56.520556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4afa4365-d940-436d-b864-f1d394d63c87 · inbound
Test-Time Scaling via Error Localization cites this paper.
Test-Time Scaling via Error Localization A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T07:28:18.384842Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.