Paper Citation Record · LEDGER

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids

As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2506.02050.

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

pith.paper-citation-record.v1
2506.02050 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:02:49.070216Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

22 of 22 outbound references displayed

verified exact3
verified fuzzy7
unresolved10
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ba773b24-6544-4327-b213-00a06a0c35fd · outbound

This paper cites PRIMAL: Pathfinding via reinforcement and imita- tion multi-agent learning,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids PRIMAL: Pathfinding via reinforcement and imita- tion multi-agent learning,

Reference 1

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T12:02:50.115300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f6d6718c-0ce0-449e-93b2-abd2bc0d6e3a · outbound

This paper cites A multi-agent reinforcement learning framework for intelligent manufac- turing with autonomous mobile robots,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids A multi-agent reinforcement learning framework for intelligent manufac- turing with autonomous mobile robots,

Reference 2

Resolution: verified exact; doi, observed 2026-08-07T12:02:49.752862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 684cebfb-530f-402b-9b3d-7f51bf405ecd · outbound

This paper cites A multi-agent deep reinforcement learning method for cooperative load frequency control of multi-area power systems,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids A multi-agent deep reinforcement learning method for cooperative load frequency control of multi-area power systems,

Reference 3

Resolution: malformed identifier; no resolver link, observed 2026-08-07T12:02:47.191731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcc5b023-ce02-44d1-9d90-607117829b47 · outbound

This paper cites Why generalization in RL is difficult: Epistemic POMDPs and implicit partial observability,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Why generalization in RL is difficult: Epistemic POMDPs and implicit partial observability,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:51.394775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a5b195d0-0aab-4607-acf5-adb5609f441b · outbound

This paper cites Managing engineer- ing systems with large state and action spaces through deep re- inforcement learning,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Managing engineer- ing systems with large state and action spaces through deep re- inforcement learning,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:47.356136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb8f6e1c-0d81-46c5-bf8e-750f6c9341d8 · outbound

This paper cites Hi- erarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Hi- erarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:51.185287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c47b6f73-0f11-4766-8a59-4bf3a6b562b4 · outbound

This paper cites The option-critic architecture,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids The option-critic architecture,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:47.525361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff098868-228c-4071-ba70-81db38fcac21 · outbound

This paper cites Hierarchical reinforcement learning with central pattern generator for enabling a quadruped robot simulator to walk on a variety of terrains,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Hierarchical reinforcement learning with central pattern generator for enabling a quadruped robot simulator to walk on a variety of terrains,

Reference 8

Resolution: verified exact; doi, observed 2026-08-07T12:02:49.519586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cc0eed5d-bf49-4ceb-9ed2-e73b7a2143de · outbound

This paper cites Hierarchical Reinforcement Learning Based on Planning Operators.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Hierarchical Reinforcement Learning Based on Planning Operators

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:47.762140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdfc6162-5d2e-4433-aa9a-c5df48169d38 · outbound

This paper cites Towards a unified theory of state abstraction for MDPs,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Towards a unified theory of state abstraction for MDPs,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:51.054101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 541046b0-2ec2-4e77-9d4c-214944c752ea · outbound

This paper cites DeepMDP: Learning continuous latent space models for representation learning,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids DeepMDP: Learning continuous latent space models for representation learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:50.846115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 745bc053-2879-47b3-a394-eea2e7e30507 · outbound

This paper cites Learning Invariant Representations for Reinforcement Learning without Reconstruction.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:48.124549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d64442d-2c84-487e-b6a9-05422ee551a5 · outbound

This paper cites Monte-Carlo planning in large POMDPs,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Monte-Carlo planning in large POMDPs,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:50.724811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0a5a2638-a7d5-4a95-ab00-2cca434cb8da · outbound

This paper cites Memory-based deep rein- forcement learning for POMDPs,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Memory-based deep rein- forcement learning for POMDPs,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:48.358902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69a54379-6bff-43dd-99dd-511513960743 · outbound

This paper cites Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:48.448037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 421da93b-2386-4471-9143-59ef34ed0b56 · outbound

This paper cites Approximate information state for approximate planning and reinforcement learning in partially observed systems.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Approximate information state for approximate planning and reinforcement learning in partially observed systems

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:48.559019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7a72e96-1230-4266-9e9d-463bb8b4ab94 · outbound

This paper cites A closer look at invalid action masking in policy gradient algorithms,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids A closer look at invalid action masking in policy gradient algorithms,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:50.578069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c6f8f742-93b3-441e-bec1-126edc8388ac · outbound

This paper cites Reinforcement learning with augmented data,.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Reinforcement learning with augmented data,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:50.429922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e8c2f43a-a0a9-4cc7-8d21-3402183c67ae · outbound

This paper cites an unresolved cited work.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:02:50.268879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b22a6630-04ae-47e3-a289-673fa63e7c13 · outbound

This paper cites an unresolved cited work.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Unresolved cited work

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:48.696580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85e08b15-f4c5-4e8d-a65d-b46aef6f7d1e · outbound

This paper cites Hierarchical Reinforcement Learning Based on Planning Operators.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Hierarchical Reinforcement Learning Based on Planning Operators

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:02:49.318576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5e37d9c1-1727-492a-8ec1-68c3528442f5 · outbound

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

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:48.964756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.