Paper Citation Record · LEDGER

Adaptive Reward Design for Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2412.10917.

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

pith.paper-citation-record.v1
2412.10917 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:37:31.607767Z

measured 27 of 27 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 2026-08-12T00:30:33.390291Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T00:30:33.977487Z

Reference resolution

26 of 26 outbound references displayed

verified exact0
verified fuzzy21
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e8b4a402-f74d-4c99-b096-e5f8f7c13658 · outbound

This paper cites Control synthesis from linear temporal logic specifications using model-free reinforcement learning.

Adaptive Reward Design for Reinforcement Learning Control synthesis from linear temporal logic specifications using model-free reinforcement learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:32.056244Z

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 5005d4f1-7527-460b-a175-e0e51bcb431c · outbound

This paper cites OpenAI Gym.

Adaptive Reward Design for Reinforcement Learning OpenAI Gym

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T15:37:31.422543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50838048-cec8-4079-b4fe-6631b96abc7d · outbound

This paper cites Overcoming exploration: Deep reinforcement learning for continuous control in cluttered environments from temporal logic specifications.

Adaptive Reward Design for Reinforcement Learning Overcoming exploration: Deep reinforcement learning for continuous control in cluttered environments from temporal logic specifications

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:32.042967Z

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 6028a52c-44e4-4ea1-9500-054d80fe0e5a · outbound

This paper cites Learning minimally-violating continuous control for infeasible linear temporal logic specifications.

Adaptive Reward Design for Reinforcement Learning Learning minimally-violating continuous control for infeasible linear temporal logic specifications

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:32.029062Z

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 a6937f62-cfca-4933-a5f8-f260ab2aaa7d · outbound

This paper cites Ltl and beyond: Formal languages for reward function specification in reinforcement learning.

Adaptive Reward Design for Reinforcement Learning Ltl and beyond: Formal languages for reward function specification in reinforcement learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:32.016510Z

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 ccd9e90b-0801-42ff-9ef7-04b9a0d11362 · outbound

This paper cites Foundations for restraining bolts: Reinforcement learning with ltlf/ldlf restraining specifications.

Adaptive Reward Design for Reinforcement Learning Foundations for restraining bolts: Reinforcement learning with ltlf/ldlf restraining specifications

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:32.003421Z

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 3560acac-53cd-4c1e-93b4-015dc9c65932 · outbound

This paper cites From language to goals: Inverse reinforcement learning for vision-based instruction following.

Adaptive Reward Design for Reinforcement Learning From language to goals: Inverse reinforcement learning for vision-based instruction following

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.987526Z

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 4c33d730-ac7d-4357-8f29-f7408f49d443 · outbound

This paper cites Reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees.

Adaptive Reward Design for Reinforcement Learning Reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.971633Z

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 28d912a4-17f9-4163-96ae-cfc3421f9ada · outbound

This paper cites Deep reinforcement learning with temporal logics.

Adaptive Reward Design for Reinforcement Learning Deep reinforcement learning with temporal logics

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.953462Z

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 6c8af446-f2ec-41a3-947d-e3028e860199 · outbound

This paper cites Reward machines: Exploiting reward function structure in reinforcement learning.

Adaptive Reward Design for Reinforcement Learning Reward machines: Exploiting reward function structure in reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.931948Z

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 6a025cf4-3809-4f04-952d-b6f39dbbf243 · outbound

This paper cites Temporal-logic-based reward shaping for continuing reinforcement learning tasks.

Adaptive Reward Design for Reinforcement Learning Temporal-logic-based reward shaping for continuing reinforcement learning tasks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.913149Z

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 55ee23ac-cc8d-4ced-833f-b07335c6ef3b · outbound

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

Adaptive Reward Design for Reinforcement Learning A composable specification language for reinforcement learning tasks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.893885Z

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 2e1da156-c139-4568-972f-3140e4b28b46 · outbound

This paper cites Compositional reinforcement learning from logical specifications.

Adaptive Reward Design for Reinforcement Learning Compositional reinforcement learning from logical specifications

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.879116Z

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 990b3a30-54ab-490c-8476-c80992c7bf68 · outbound

This paper cites Model checking of safety properties.

Adaptive Reward Design for Reinforcement Learning Model checking of safety properties

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.862535Z

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 c8d86e92-4313-4978-9209-df85a676621b · outbound

This paper cites Probabilistic planning with formal performance guarantees for mobile service robots.

Adaptive Reward Design for Reinforcement Learning Probabilistic planning with formal performance guarantees for mobile service robots

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.847530Z

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 e8bef7a2-3962-4188-bddf-15a9f20ee938 · outbound

This paper cites Reinforcement learning with temporal logic rewards.

Adaptive Reward Design for Reinforcement Learning Reinforcement learning with temporal logic rewards

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.833861Z

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 4c2b5c61-fb91-46b8-9fda-e4254a406a0f · outbound

This paper cites Continuous control with deep reinforcement learning.

Adaptive Reward Design for Reinforcement Learning Continuous control with deep reinforcement learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T15:37:31.552290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8fad458-da6e-45ce-b701-5681eb729509 · outbound

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

Adaptive Reward Design for Reinforcement Learning Human-level control through deep reinforcement learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T15:37:31.556926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a8b3ba6-fda5-4c02-a686-379596ee1bf7 · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

Adaptive Reward Design for Reinforcement Learning Asynchronous methods for deep reinforcement learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.797080Z

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 c0e50507-434f-4da3-b512-3c9c80236a2b · outbound

This paper cites Algorithms for inverse reinforcement learning.

Adaptive Reward Design for Reinforcement Learning Algorithms for inverse reinforcement learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.778765Z

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 08ec08ef-dc8d-42a8-80d9-42aabc55c140 · outbound

This paper cites Policy invariance under reward transformations: Theory and application to reward shaping.

Adaptive Reward Design for Reinforcement Learning Policy invariance under reward transformations: Theory and application to reward shaping

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.757627Z

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 38404a0c-da5b-459f-87bb-38bf87f9a166 · outbound

This paper cites The temporal semantics of concurrent programs.

Adaptive Reward Design for Reinforcement Learning The temporal semantics of concurrent programs

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.733684Z

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 b79e5b12-0680-40e0-9c64-691dae414074 · outbound

This paper cites Stable-baselines3: Reliable reinforcement learning implementations.

Adaptive Reward Design for Reinforcement Learning Stable-baselines3: Reliable reinforcement learning implementations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.717040Z

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 74e00a7c-6247-4e1e-9cee-cc5afc606738 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Adaptive Reward Design for Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T15:37:31.597864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 699c7cc4-085b-4d80-aa89-8ded6c4ed214 · outbound

This paper cites Deep reinforcement learning with double q-learning.

Adaptive Reward Design for Reinforcement Learning Deep reinforcement learning with double q-learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:37:31.701426Z

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 f1af1b0f-3017-46e2-9fee-6bf33ef907e7 · outbound

This paper cites A survey of preference-based reinforcement learning methods.

Adaptive Reward Design for Reinforcement Learning A survey of preference-based reinforcement learning methods

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T15:37:31.607767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7def0c9b-206f-4f04-bbc5-6b2628a79826 · inbound

A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning cites this paper.

A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Adaptive Reward Design for Reinforcement Learning

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-12T00:30:33.983121Z

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.