Paper Citation Record · LEDGER

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 1 inbound Pith citation observation for arXiv:2507.15287.

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

pith.paper-citation-record.v1
2507.15287 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:42:03.341085Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T19:05:15.708770Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T19:06:30.602990Z

Reference resolution

16 of 16 outbound references displayed

verified exact1
verified fuzzy3
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 771a2466-faf3-4fce-a4ec-1d9a78f44860 · outbound

This paper cites A survey on intrinsic motivation in reinforcement learning.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning A survey on intrinsic motivation in reinforcement learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.196251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5ca29ce-5881-4ccc-bb21-ecf56bad327d · outbound

This paper cites Making Efficient Use of Demonstrations to Solve Hard Exploration Problems.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Making Efficient Use of Demonstrations to Solve Hard Exploration Problems

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.259833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea5b3570-07fc-4872-bb6b-313509f92ba0 · outbound

This paper cites Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.276186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7fdcd91-fafd-4599-9445-694075a5d383 · outbound

This paper cites Adversarial Imitation Learning from Incomplete Demonstrations.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Adversarial Imitation Learning from Incomplete Demonstrations

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:42:03.674069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 73993afe-7cd8-40dd-8d48-f2bc7bafdf6a · outbound

This paper cites Exploration and Anti-Exploration with Distributional Random Network Distillation.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Exploration and Anti-Exploration with Distributional Random Network Distillation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.316515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abccf5f8-e663-4010-96ac-0ada649605aa · outbound

This paper cites RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.329662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 566b7923-0178-4262-b848-56a97cad06d8 · outbound

This paper cites Rui Zhao and V olker Tresp.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Rui Zhao and V olker Tresp

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.336321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fe26d1b-d790-4e1f-9bc7-f28af47c3f64 · outbound

This paper cites An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks

Reference 1989

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T15:42:03.757615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1fddea10-d7da-4f3e-82f9-c2de8a8cb22c · outbound

This paper cites Behavioral Cloning from Observation.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Behavioral Cloning from Observation

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.304388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d9b7790-6cdd-4a25-9deb-11ae6a7a5595 · outbound

This paper cites Exploration by Random Network Distillation.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Exploration by Random Network Distillation

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.203959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b87e366-a953-463e-9fa6-0c5e4cd585ef · outbound

This paper cites Explorative imitation learning: A path signature approach for continuous environments.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Explorative imitation learning: A path signature approach for continuous environments

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:04.028789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 86238447-d171-418a-a854-4743cae0ab33 · outbound

This paper cites Sparsedice: Imitation learning for temporally sparse data via regularization.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Sparsedice: Imitation learning for temporally sparse data via regularization

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:04.059332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1f2cc44f-ff3d-4de6-97d8-345a2f33353b · outbound

This paper cites State Alignment-based Imitation Learning.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning State Alignment-based Imitation Learning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.251702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fecf03e2-c4df-43ab-baf7-4684449c3f14 · outbound

This paper cites F Grid world We present qualitative results in a gridworld with random walls, where the agent can move in any direction.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning F Grid world We present qualitative results in a gridworld with random walls, where the agent can move in any direction

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:03.993208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d94434a7-9170-43dc-af91-c00316facab5 · outbound

This paper cites Learning Robust Rewards with Adversarial Inverse Reinforcement Learning.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.223862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f348d27f-a54a-432c-b92b-dea35f4c95da · outbound

This paper cites Soft Actor-Critic Algorithms and Applications.

Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning Soft Actor-Critic Algorithms and Applications

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T15:42:03.243618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation b6d982f6-6536-4e7d-8657-a6dbc3977d72 · inbound

The Alignment Flywheel: A Governance-Centric Hybrid MAS for Architecture-Agnostic Safety cites this paper.

The Alignment Flywheel: A Governance-Centric Hybrid MAS for Architecture-Agnostic Safety Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:06:30.605792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.