Paper Citation Record · LEDGER

Mapping State Space using Landmarks for Universal Goal Reaching

As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1908.05451.

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

pith.paper-citation-record.v1
1908.05451 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:16:22.503217Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

37 of 37 outbound references displayed

verified exact3
verified fuzzy16
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9e2e0796-ac37-4b45-a005-ae9ee557f52a · outbound

This paper cites Efﬁcient memory-based learning for robot control.

Mapping State Space using Landmarks for Universal Goal Reaching Efﬁcient memory-based learning for robot control

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:23.001178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 82c48303-614f-498d-8e0b-e2c75c825440 · outbound

This paper cites Hierarchical Reinforcement Learning with Hindsight.

Mapping State Space using Landmarks for Universal Goal Reaching Hierarchical Reinforcement Learning with Hindsight

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-14T13:16:22.730738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d786f4d2-f66a-4f0e-95b3-5332842228f4 · outbound

This paper cites Universal value function approxi- mators.

Mapping State Space using Landmarks for Universal Goal Reaching Universal value function approxi- mators

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.988991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f2b8d0f0-09ef-4354-b476-46a8dcb6b77d · outbound

This paper cites Hindsight experience replay.

Mapping State Space using Landmarks for Universal Goal Reaching Hindsight experience replay

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.229427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 946c7bd1-499b-467e-b6a4-21ffdfb2904d · outbound

This paper cites an unresolved cited work.

Mapping State Space using Landmarks for Universal Goal Reaching Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:16:22.964490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 09703a54-34f9-46a4-b40c-26b62b50d195 · outbound

This paper cites Sparse multidimensional scaling using landmark points.

Mapping State Space using Landmarks for Universal Goal Reaching Sparse multidimensional scaling using landmark points

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.950291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ec0593d5-b0dc-4991-8cf4-e93bc49efa38 · outbound

This paper cites Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction.

Mapping State Space using Landmarks for Universal Goal Reaching Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.936749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5ad2c7e8-4ddd-4198-a2cf-f83f0fd3fb5f · outbound

This paper cites Universal agent for disentangling environments and tasks.

Mapping State Space using Landmarks for Universal Goal Reaching Universal agent for disentangling environments and tasks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.923073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d1dc4fab-bde4-4949-b068-1a063cbeee08 · outbound

This paper cites Temporal Difference Models: Model-Free Deep RL for Model-Based Control.

Mapping State Space using Landmarks for Universal Goal Reaching Temporal Difference Models: Model-Free Deep RL for Model-Based Control

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.249705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5048dc90-f5d5-4174-b66b-424baaa5aecf · outbound

This paper cites Continuous control with deep reinforcement learning.

Mapping State Space using Landmarks for Universal Goal Reaching Continuous control with deep reinforcement learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.253706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7e443ee-1edc-48b3-b54d-6382a538640b · outbound

This paper cites Learning Latent Dynamics for Planning from Pixels.

Mapping State Space using Landmarks for Universal Goal Reaching Learning Latent Dynamics for Planning from Pixels

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.257797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07e9d644-f6f6-4ac9-b21a-8c440b473dab · outbound

This paper cites Value prediction network.

Mapping State Space using Landmarks for Universal Goal Reaching Value prediction network

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.909576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d0db6fe5-ccfa-4516-ba7d-7ec67d930a16 · outbound

This paper cites an unresolved cited work.

Mapping State Space using Landmarks for Universal Goal Reaching Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:16:22.896790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 07e477f6-8fd6-47a0-b7f8-fee9cf35ec1b · outbound

This paper cites Model-Based Planning with Discrete and Continuous Actions.

Mapping State Space using Landmarks for Universal Goal Reaching Model-Based Planning with Discrete and Continuous Actions

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.273307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fde6be5-179e-4b14-8aef-82b544017712 · outbound

This paper cites Universal Planning Networks.

Mapping State Space using Landmarks for Universal Goal Reaching Universal Planning Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.278636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ac9d929-707c-4d4e-b909-e868a6347a27 · outbound

This paper cites Unsupervised Visuomotor Control through Distributional Planning Networks.

Mapping State Space using Landmarks for Universal Goal Reaching Unsupervised Visuomotor Control through Distributional Planning Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.283258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db53c5c4-472b-4f0e-a75c-9c20c16ecc1b · outbound

This paper cites Modeling the long term future in model-based reinforcement learning.

Mapping State Space using Landmarks for Universal Goal Reaching Modeling the long term future in model-based reinforcement learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.883439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f0c84fb4-a276-4951-837e-13a5b4decd30 · outbound

This paper cites Value iteration networks.

Mapping State Space using Landmarks for Universal Goal Reaching Value iteration networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.869300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f52405c0-7606-4423-9990-04c73cb8426c · outbound

This paper cites Gradient-based learning applied to document recognition.

Mapping State Space using Landmarks for Universal Goal Reaching Gradient-based learning applied to document recognition

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.296869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90dabf1b-8da3-4b23-bdaf-c709999edb0b · outbound

This paper cites A formal basis for the heuristic determination of minimum cost paths.

Mapping State Space using Landmarks for Universal Goal Reaching A formal basis for the heuristic determination of minimum cost paths

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.848085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b5f2a9b6-cc29-4d26-8a89-69b1c5a07e93 · outbound

This paper cites Rapidly-exploring random trees: A new tool for path planning.

Mapping State Space using Landmarks for Universal Goal Reaching Rapidly-exploring random trees: A new tool for path planning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.836189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 26f341e6-ac7e-4232-93df-5a1c1bff63f0 · outbound

This paper cites Probabilistic roadmaps for path planning in high-dimensional conﬁguration spaces, volume 1994.

Mapping State Space using Landmarks for Universal Goal Reaching Probabilistic roadmaps for path planning in high-dimensional conﬁguration spaces, volume 1994

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.822562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 62f0b633-45c5-4c45-93be-1a194a5d94bc · outbound

This paper cites Robot Motion Planning in Learned Latent Spaces.

Mapping State Space using Landmarks for Universal Goal Reaching Robot Motion Planning in Learned Latent Spaces

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.441074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94d70fe2-53b3-4127-a46b-1d634c5517d9 · outbound

This paper cites Deeply Informed Neural Sampling for Robot Motion Planning.

Mapping State Space using Landmarks for Universal Goal Reaching Deeply Informed Neural Sampling for Robot Motion Planning

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T13:16:22.633003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e2b0fcc0-3bc7-4280-b3ed-79830212e0c2 · outbound

This paper cites Towards Learning Abstract Representations for Locomotion Planning in High-dimensional State Spaces.

Mapping State Space using Landmarks for Universal Goal Reaching Towards Learning Abstract Representations for Locomotion Planning in High-dimensional State Spaces

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-14T13:16:22.616387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 84ef40d1-21de-4324-a013-b59ee8826aa8 · outbound

This paper cites Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning.

Mapping State Space using Landmarks for Universal Goal Reaching Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.809055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1c2ede4b-480d-420f-8918-f26e3c671f95 · outbound

This paper cites Semi-parametric Topological Memory for Navigation.

Mapping State Space using Landmarks for Universal Goal Reaching Semi-parametric Topological Memory for Navigation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.459812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eacc1d40-a780-43ad-8b9e-12959fa1bd0e · outbound

This paper cites Composable Planning with Attributes.

Mapping State Space using Landmarks for Universal Goal Reaching Composable Planning with Attributes

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-14T13:16:22.582060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ea2518ea-6d4a-4671-835d-4bb1558e7b44 · outbound

This paper cites Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation.

Mapping State Space using Landmarks for Universal Goal Reaching Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.797433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c59384b6-c40e-4f0e-ac74-4bab04a4a568 · outbound

This paper cites Data-efﬁcient hierarchical reinforcement learning.

Mapping State Space using Landmarks for Universal Goal Reaching Data-efﬁcient hierarchical reinforcement learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.785340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 65e63715-408f-46ac-8106-7c3e4769e928 · outbound

This paper cites Benchmarking Deep Reinforcement Learning for Continuous Control.

Mapping State Space using Landmarks for Universal Goal Reaching Benchmarking Deep Reinforcement Learning for Continuous Control

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.477055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f872bb6-8932-4748-abe7-2550875d7c9b · outbound

This paper cites Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research.

Mapping State Space using Landmarks for Universal Goal Reaching Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.480974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2951b6ff-bbe0-4fca-8628-84bcb749f63d · outbound

This paper cites Computing the shortest path: A search meets graph theory.

Mapping State Space using Landmarks for Universal Goal Reaching Computing the shortest path: A search meets graph theory

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.771645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9694827b-a04d-4554-90cf-5e87f4c4220e · outbound

This paper cites k-means++: The advantages of careful seeding.

Mapping State Space using Landmarks for Universal Goal Reaching k-means++: The advantages of careful seeding

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.488871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 392b43b0-7041-44bc-bade-48e76904c61a · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Mapping State Space using Landmarks for Universal Goal Reaching Playing Atari with Deep Reinforcement Learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.494165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 061550b6-6102-46dd-8c52-c1e0489d682f · outbound

This paper cites Mujoco: A physics engine for model-based control.

Mapping State Space using Landmarks for Universal Goal Reaching Mujoco: A physics engine for model-based control

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:22.498838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46610cca-ae10-4c7b-9e8f-59dc62ffc71a · outbound

This paper cites Openai gym, 2016.

Mapping State Space using Landmarks for Universal Goal Reaching Openai gym, 2016

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:16:22.742440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.