Paper Citation Record · LEDGER

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning

As of 19 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 1 inbound Pith citation observation for arXiv:2411.19133.

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

pith.paper-citation-record.v1
2411.19133 v2

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:32:43.014855Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T10:27:47.896922Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T09:17:48.457499Z

Reference resolution

11 of 11 outbound references displayed

verified exact0
verified fuzzy9
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8ae020c8-0da9-4b92-95d3-75981e0ca466 · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Domain randomization for transferring deep neural networks from simulation to the real world

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:32:43.125447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9c38f693-fa4e-4413-8156-efedb6702e1e · outbound

This paper cites Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T10:32:42.982760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cfc7c31-c056-474e-9669-9bfdf5b40c57 · outbound

This paper cites Efficient off-policy meta-reinforcement learning via probabilistic context variables.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Efficient off-policy meta-reinforcement learning via probabilistic context variables

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:32:43.117090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d40bdaa7-5b52-4981-8085-4003c875eea6 · outbound

This paper cites Varibad: A very good method for Bayes-adaptive deep RL via meta-learning.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Varibad: A very good method for Bayes-adaptive deep RL via meta-learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:32:43.107766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6203dc7-5c87-4ad9-a9c1-856db9515ebc · outbound

This paper cites Fleet control using coregionalized gaussian process policy iteration.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Fleet control using coregionalized gaussian process policy iteration

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:32:43.098381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fb883e69-68db-4271-bcaf-336c89079b3b · outbound

This paper cites Recurrent world models facilitate policy evolution.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Recurrent world models facilitate policy evolution

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:32:43.089608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f0ceb85-24e5-4086-9652-68ef87acb824 · outbound

This paper cites Off-policy deep reinforcement learning without exploration.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Off-policy deep reinforcement learning without exploration

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:32:43.080690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 402a4b1c-c5b2-4101-9cfa-b4172a00738e · outbound

This paper cites Benchmarking Batch Deep Reinforcement Learning Algorithms.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T10:32:43.004752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f80ecd9-9e3e-4e22-ae19-d027fba3ea47 · outbound

This paper cites User-interactive offline reinforce- ment learning.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning User-interactive offline reinforce- ment learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:32:43.071323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a36dd1b1-3c0d-4c3c-8201-180206775318 · outbound

This paper cites Neuronlike adaptive ele- ments that can solve difficult learning control problems.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Neuronlike adaptive ele- ments that can solve difficult learning control problems

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:32:43.062863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9d18b34f-6a39-412a-bf83-8899e6fe6add · outbound

This paper cites Human-level control through deep reinforcement learning.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Human-level control through deep reinforcement learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:32:43.053254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation bd2cd6d0-f26f-4f9f-9ac7-4b34a288f68c · inbound

Implicit Neural Representations of Individual Behavior cites this paper.

Implicit Neural Representations of Individual Behavior TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:17:48.458924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.