Paper Citation Record · LEDGER

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications

As of 15 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2501.18848.

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

pith.paper-citation-record.v1
2501.18848 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:20:04.901141Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

30 of 30 outbound references displayed

verified exact1
verified fuzzy23
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dab0460f-32a5-4dda-a7f1-8c7bc5d6e6b1 · outbound

This paper cites Reinforcement learning in robotics: A survey,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Reinforcement learning in robotics: A survey,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T22:20:04.804315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14c4420c-ebbb-4d2d-9281-222bd12bfa79 · outbound

This paper cites Brachman and H.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Brachman and H

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.187117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b9eec02a-95ea-43e9-8beb-4c2f51ddfbaa · outbound

This paper cites SDRL: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications SDRL: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.178778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5663ffd4-274e-4257-b260-3c6b75924eea · outbound

This paper cites Symbolic plans as high-level instructions for reinforcement learning,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Symbolic plans as high-level instructions for reinforcement learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.163545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3c2d4ba6-4ab3-44f0-a69d-8a2746246a33 · outbound

This paper cites Creativity of ai: Automatic symbolic option discovery for facilitating deep reinforcement learning,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Creativity of ai: Automatic symbolic option discovery for facilitating deep reinforcement learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.154658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4b3ce914-0173-4cb9-bab0-9efeda2b1380 · outbound

This paper cites A survey on interpretable reinforcement learning,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications A survey on interpretable reinforcement learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.145459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 69741356-fb86-4f7f-9c02-15387fa8bb38 · outbound

This paper cites PDDL-the planning domain definition language,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications PDDL-the planning domain definition language,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.136651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eb99c8a1-735b-417e-879e-971c24a1d126 · outbound

This paper cites The temporal logic of programs,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications The temporal logic of programs,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.127823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0fa259e7-0277-45e8-b95a-4c9011229480 · outbound

This paper cites Teaching multiple tasks to an RL agent using LTL,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Teaching multiple tasks to an RL agent using LTL,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.119309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aa02727c-571c-458b-bb58-be9a635169a7 · outbound

This paper cites Using reward machines for high-level task specification and decomposition in reinforcement learning,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Using reward machines for high-level task specification and decomposition in reinforcement learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.109270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d9ae4d23-fc77-4d4d-abf8-a622162be668 · outbound

This paper cites LTL and Beyond: Formal languages for reward function specification in reinforcement learning.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications LTL and Beyond: Formal languages for reward function specification in reinforcement learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.100247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6f3d96e1-6c58-47e3-8116-61eb21b5fc59 · outbound

This paper cites LTL2Action: Generalizing LTL instructions for Multi-Task RL,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications LTL2Action: Generalizing LTL instructions for Multi-Task RL,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.091359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d882dbd8-d7da-4a10-8bf2-54b082ee18dd · outbound

This paper cites Reinforcement learn- ing of action and query policies with LTL instructions under uncertain event detector,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Reinforcement learn- ing of action and query policies with LTL instructions under uncertain event detector,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.082681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4fda7d4e-4ac6-4292-9cdb-cc3dea326dbf · outbound

This paper cites A composable specification language for reinforcement learning tasks,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications A composable specification language for reinforcement learning tasks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.072798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1d2c4ee2-8947-4085-9429-9bdb589e8f48 · outbound

This paper cites Context-aware temporal logic for probabilistic systems,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Context-aware temporal logic for probabilistic systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.063078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 70844f87-6416-4ad5-b884-3c65ebbed6b3 · outbound

This paper cites Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.053799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 84fe28b2-77e6-4781-8610-b178a51f425e · outbound

This paper cites Multi-task reinforcement learning with soft modularization,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Multi-task reinforcement learning with soft modularization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.044673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fdbfaffa-bc61-4bed-ae95-568dc7e412d9 · outbound

This paper cites Multi-task reinforcement learn- ing with context-based representations,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Multi-task reinforcement learn- ing with context-based representations,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.035339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 300beee1-907d-4a55-ba42-994337d64086 · outbound

This paper cites Perceiver-actor: A multi- task transformer for robotic manipulation,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Perceiver-actor: A multi- task transformer for robotic manipulation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.025911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6883746d-5fe6-4acb-b1e7-64d8571d98b5 · outbound

This paper cites Baier and J.-P.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Baier and J.-P

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T22:20:04.869217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afbe5d86-f379-408d-945a-6aa33951174e · outbound

This paper cites Model checking of safety properties,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Model checking of safety properties,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.011532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation df999ba8-c686-4928-983f-4cb7237a9b64 · outbound

This paper cites Optimal policy generation for partially satisfiable co-safe LTL specifications,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Optimal policy generation for partially satisfiable co-safe LTL specifications,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:05.001726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6f27ca28-1f9b-485f-ab0b-e920d52b3bb1 · outbound

This paper cites Using temporal logics to express search control knowledge for planning,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Using temporal logics to express search control knowledge for planning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:04.991581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d613d888-7f5d-459f-8d51-83743210837f · outbound

This paper cites Provably efficient RL with rich observations via latent state decoding,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Provably efficient RL with rich observations via latent state decoding,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:04.982204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 291c9de8-ff4a-463d-b0a6-36c4d9b94e97 · outbound

This paper cites Film: Visual reasoning with a general conditioning layer,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Film: Visual reasoning with a general conditioning layer,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T22:20:04.884048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce031c10-1d67-46eb-81af-bffd82e76de9 · outbound

This paper cites A survey on curriculum learning,.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications A survey on curriculum learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:20:04.966722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1c98549b-888f-40b8-afcc-01aad1e4e3f6 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Proximal Policy Optimization Algorithms

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T22:20:04.890155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0c901bf-0618-4309-8938-32634bdd20f6 · outbound

This paper cites Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T22:20:04.893504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36a1f04e-283d-40c1-8c2a-3ba8835468a5 · outbound

This paper cites Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T22:20:04.896956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b72f5c24-6d8e-4d1b-b76e-5a8f737ee845 · outbound

This paper cites Guided Policy Search for Parameterized Skills using Adverbs.

Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications Guided Policy Search for Parameterized Skills using Adverbs

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-09T22:20:04.932332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.