Paper Citation Record · LEDGER

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead

As of 10 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2506.07054.

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

pith.paper-citation-record.v1
2506.07054 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:53:15.489700Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T08:12:57.430291Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T08:17:36.523927Z

Reference resolution

22 of 22 outbound references displayed

verified exact1
verified fuzzy16
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 56c0e748-0fab-45bb-bd14-b08996d7944f · outbound

This paper cites On the theory of policy gradient methods: Optimality, approximation, and distribution shift.The Journal of Machine Learning Research, 22(1):4431–4506, 2021.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead On the theory of policy gradient methods: Optimality, approximation, and distribution shift.The Journal of Machine Learning Research, 22(1):4431–4506, 2021

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:53:15.402520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3422e75-af8d-4b42-81e8-c13b283b4395 · outbound

This paper cites Natural actor- critic algorithms.Automatica, 45:2471–2482, 11 2009.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Natural actor- critic algorithms.Automatica, 45:2471–2482, 11 2009

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.739448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2a85ad48-e844-4c5d-932c-ce636f67b2a4 · outbound

This paper cites Finite-time analysis of single-timescale actor-critic.Advances in Neural Information Processing Systems, 36, 2024.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Finite-time analysis of single-timescale actor-critic.Advances in Neural Information Processing Systems, 36, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.728469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 43763294-3e93-43a4-b32b-352057071b5e · outbound

This paper cites Bellemare.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Bellemare

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.717309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 81b3254c-48b1-4fee-9ddf-acd97723552c · outbound

This paper cites Beyond the One Step Greedy Approach in Reinforcement Learning.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Beyond the One Step Greedy Approach in Reinforcement Learning

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:53:15.526463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 51ece1e1-2ea5-4ff7-9766-b6dc50795aea · outbound

This paper cites Kakade, Karan Singh, and Abby Van Soest.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Kakade, Karan Singh, and Abby Van Soest

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.706317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 57b80b14-6b61-46d9-83f0-f94a58620f0d · outbound

This paper cites Actor-critic algorithms.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Actor-critic algorithms

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.695828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation af50b33e-cec3-45cf-a19d-9e94afbfea7e · outbound

This paper cites Policy gradient with tree search (PGTS) in reinforcement learning evades local maxima.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Policy gradient with tree search (PGTS) in reinforcement learning evades local maxima

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.684281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b7909ac8-de03-407f-80da-d6f6a105ef17 · outbound

This paper cites an unresolved cited work.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:53:15.672487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a08650c2-ebf2-4ecf-9276-7e15532c7613 · outbound

This paper cites Elementary analysis of policy gradient methods, 2024.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Elementary analysis of policy gradient methods, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.662105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bc90bb24-e7bf-416f-8c44-9edb6cbf28f3 · outbound

This paper cites On the global conver- gence rates of softmax policy gradient methods.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead On the global conver- gence rates of softmax policy gradient methods

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.651383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1e918b11-ad93-439d-8d9c-730f3fba57df · outbound

This paper cites Rusu, Joel Veness, Marc G.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Rusu, Joel Veness, Marc G

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.640881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7eb16917-a3a7-4ac4-8ac6-0ec7260376bf · outbound

This paper cites Policy mirror descent with lookahead, 2024.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Policy mirror descent with lookahead, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.629318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a2d3ff54-86d7-456c-9820-2ef2c66ea579 · outbound

This paper cites John Wiley & Sons, 2014.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead John Wiley & Sons, 2014

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:53:15.457879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec476048-53ed-420f-823f-3c0793c92f18 · outbound

This paper cites Trust region policy optimization.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Trust region policy optimization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:53:15.461456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34db7301-cfab-47c5-b2da-56ab6f3284c1 · outbound

This paper cites Jordan, and Pieter Abbeel.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Jordan, and Pieter Abbeel

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.604094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 68dbb265-dee3-47f6-984a-c97c6eda7e5c · outbound

This paper cites A general reinforcement learning algorithm that masters chess, shogi, and go through self-play.Science, 362(6419):1140–1144, 2018.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead A general reinforcement learning algorithm that masters chess, shogi, and go through self-play.Science, 362(6419):1140–1144, 2018

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.593271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3dcbdbcf-1631-4f47-aaa6-d304346c9057 · outbound

This paper cites Sutton and Andrew G.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Sutton and Andrew G

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:53:15.473939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c23fe72e-c3e4-43d5-a58f-114fe4743eb8 · outbound

This paper cites Policy gradient methods for reinforcement learning with function approximation.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Policy gradient methods for reinforcement learning with function approximation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.572872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 95b5e946-8bc8-42ee-9e6e-91def601df2d · outbound

This paper cites Policy gradient methods for reinforcement learning with function approximation.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead Policy gradient methods for reinforcement learning with function approximation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.561490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a92f577c-8c28-4350-b9fc-696876e7c04e · outbound

This paper cites On the convergence rates of policy gradient methods, 2022.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead On the convergence rates of policy gradient methods, 2022

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.549983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 86e9b9f1-d77b-4dec-a355-26195e177e7e · outbound

This paper cites On the convergence rates of policy gradient methods.Journal of Machine Learning Research, 23(282):1–36, 2022.

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead On the convergence rates of policy gradient methods.Journal of Machine Learning Research, 23(282):1–36, 2022

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:15.539115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation ada1f7a4-89f3-4e80-98c2-0ba288ee1c01 · inbound

Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum cites this paper.

Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:17:36.525446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.