Paper Citation Record · LEDGER

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

As of 12 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 52 inbound Pith citation observations for arXiv:1911.08265.

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

pith.paper-citation-record.v1
1911.08265 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T23:57:02.653534Z

measured 105 of 105 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 52 of 52 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:28:18.643656Z

measured 1 of 1 external citation measurements

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

Source: doi_reference, observed 2026-07-11T00:47:41.987004Z

Reference resolution

53 of 53 outbound references displayed

verified exact9
verified fuzzy40
unresolved2
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

1051
doi_reference, observed 2026-07-11T00:47:41.987004Z

Outbound references

Observation f67b3ed9-2b9a-4e02-8ee7-3ffa90927141 · outbound

This paper cites Surprising Negative Results for Generative Adversarial Tree Search.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Surprising Negative Results for Generative Adversarial Tree Search

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:57:02.726748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 47fdb076-c1bf-4440-866a-b7fa821c06f8 · outbound

This paper cites The arcade learning environment: An evaluation platform for general agents.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model The arcade learning environment: An evaluation platform for general agents

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.830260Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 41dad89d-0db1-412d-b1b4-ee0cac0fee35 · outbound

This paper cites Superhuman ai for heads-up no-limit poker: Libratus beats top profes- sionals.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Superhuman ai for heads-up no-limit poker: Libratus beats top profes- sionals

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.832408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 850ad890-cefd-4d17-be50-e8ff5057c1ec · outbound

This paper cites Learning and Querying Fast Generative Models for Reinforcement Learning.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Learning and Querying Fast Generative Models for Reinforcement Learning

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-16T23:57:02.695798Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6d8453fa-62f3-4265-9019-b7273c2e6d3d · outbound

This paper cites Joseph Hoane, Jr., and Feng-hsiung Hsu.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Joseph Hoane, Jr., and Feng-hsiung Hsu

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.834230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1f130c5c-ab9d-40fb-8249-184f0e55837b · outbound

This paper cites an unresolved cited work.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-05-16T23:57:02.836680Z

Source-reported events for the cited work

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Observation 69af9c69-2ff6-442e-8e02-16b1645a474d · outbound

This paper cites Efﬁcient selectivity and backup operators in monte-carlo tree search.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Efﬁcient selectivity and backup operators in monte-carlo tree search

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.838898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 14fb4b99-a142-43fe-9565-25cd41a5420e · outbound

This paper cites Deisenroth and CE.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Deisenroth and CE

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.841113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f283646a-e748-4ee6-8e91-d7e05a13b041 · outbound

This paper cites Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.843539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 66298709-2fac-49e7-95c6-fd3805e3aeea · outbound

This paper cites TreeQN and ATreec: Differ- entiable tree planning for deep reinforcement learning.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model TreeQN and ATreec: Differ- entiable tree planning for deep reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.846106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 958132af-1f46-4bed-91a0-69e5fcbd0f69 · outbound

This paper cites Bellemare.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Bellemare

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.848487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7814ce43-c286-4e56-a7d9-e68e412a13e9 · outbound

This paper cites https://cloud.google.com/tpu/.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model https://cloud.google.com/tpu/

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.729764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f8f0c2d2-e132-4e02-a646-40e90e41e41c · outbound

This paper cites Recurrent world models facilitate policy evolution.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Recurrent world models facilitate policy evolution

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.733364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85d24a34-2b7e-4ae5-b2de-7912233cd40c · outbound

This paper cites Learning Latent Dynamics for Planning from Pixels.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Learning Latent Dynamics for Planning from Pixels

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-16T23:57:02.700121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8eb01c14-f80d-4f4a-8494-ffe15e532c27 · outbound

This paper cites Identity mappings in deep residual networks.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Identity mappings in deep residual networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.736815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 96aeb3e6-2412-4533-ba0d-d0dd688d9ec4 · outbound

This paper cites Learning con- tinuous control policies by stochastic value gradients.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Learning con- tinuous control policies by stochastic value gradients

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.740106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d9167401-482e-428e-a86f-28c812c8693d · outbound

This paper cites Rainbow: Combining improvements in deep reinforcement learning.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Rainbow: Combining improvements in deep reinforcement learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.743289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a6f1298d-f5f0-432b-8251-7aaba8562357 · outbound

This paper cites Distributed prioritized experience replay.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Distributed prioritized experience replay

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.746293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c6802c97-daf4-4b12-9d7e-9a0ff4941d5d · outbound

This paper cites Reinforcement Learning with Unsupervised Auxiliary Tasks.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Reinforcement Learning with Unsupervised Auxiliary Tasks

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-16T23:57:02.713011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e0fad99e-53ff-4f40-84a9-9bcdfe01dde8 · outbound

This paper cites Model-Based Reinforcement Learning for Atari.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Model-Based Reinforcement Learning for Atari

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:57:02.717722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 17072e63-8556-44b8-af32-8ef51e2c9250 · outbound

This paper cites Recurrent experience replay in distributed reinforcement learning.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Recurrent experience replay in distributed reinforcement learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.749167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3c1bcef0-c9ab-4991-a176-1a6aa3f58506 · outbound

This paper cites Bandit based monte-carlo planning.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Bandit based monte-carlo planning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.752193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 254031f8-f6bf-46ad-a21f-8db1998b22e5 · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural networks.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Imagenet classiﬁcation with deep convolutional neural networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.755470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 37613040-69a5-4e44-9cf7-e9908b9597af · outbound

This paper cites Learning neural network policies with guided policy search under un- known dynamics.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Learning neural network policies with guided policy search under un- known dynamics

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.758423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bb6b7c9b-a100-40b7-9dbd-19cdde420b81 · outbound

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

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Human-level control through deep reinforcement learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.762032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f0b29ec6-617d-490e-a37f-4bf07468b120 · outbound

This paper cites Deepstack: Expert-level artiﬁcial intelligence in heads-up no-limit poker.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Deepstack: Expert-level artiﬁcial intelligence in heads-up no-limit poker

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.765092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8bf9cb1f-2c11-4458-81f8-48c7c7903ee3 · outbound

This paper cites Massively Parallel Methods for Deep Reinforcement Learning.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Massively Parallel Methods for Deep Reinforcement Learning

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-16T23:57:02.708622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3f86b047-0301-4fe5-b0e0-69c65159aa59 · outbound

This paper cites Value prediction network.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Value prediction network

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.767646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bb8e60d7-3d76-4d96-b782-dddd162d705e · outbound

This paper cites Openai ﬁve.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Openai ﬁve

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.770558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bc4c4361-9b85-4b13-83bb-c101922b820c · outbound

This paper cites Observe and Look Further: Achieving Consistent Performance on Atari.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Observe and Look Further: Achieving Consistent Performance on Atari

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-16T23:57:02.685637Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 87ab1b69-46d2-44c8-98c0-cde3faa70176 · outbound

This paper cites Puterman.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Puterman

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.773269Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 386f2f18-4978-4100-86b1-90169a213fad · outbound

This paper cites Multi-armed bandits with episode context.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Multi-armed bandits with episode context

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.776344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9779a4cf-d356-469d-be48-7cb230c2d7c4 · outbound

This paper cites Single-player monte-carlo tree search.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Single-player monte-carlo tree search

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.778927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 093952d3-7e07-4641-a151-c4b114d1909f · outbound

This paper cites A world championship caliber checkers program.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model A world championship caliber checkers program

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.781764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8ed3d4ed-79f3-4d03-b5b6-ada6e2e727c5 · outbound

This paper cites Prioritized experience replay.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Prioritized experience replay

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.784350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4a6996f9-e7bc-4557-b09f-92ebef272e44 · outbound

This paper cites Off-Policy Actor-Critic with Shared Experience Replay.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Off-Policy Actor-Critic with Shared Experience Replay

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:57:02.722726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3fd9c814-6b8e-4701-8fd4-ddc0d35ad7fc · outbound

This paper cites Planning chemical syntheses with deep neural networks and symbolic ai.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Planning chemical syntheses with deep neural networks and symbolic ai

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.787323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b9c6b31f-1f78-4346-86b2-c196ae142330 · outbound

This paper cites an unresolved cited work.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-05-16T23:57:02.789649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bfb86e4b-0720-4e25-b019-3193b76aba26 · outbound

This paper cites A general reinforcement learning algorithm that masters chess, shogi, and go through self-play.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model A general reinforcement learning algorithm that masters chess, shogi, and go through self-play

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.792280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6b468431-02d3-460c-860a-e5ecb0f13ac5 · outbound

This paper cites Mastering the game of go without human knowledge.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Mastering the game of go without human knowledge

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.795248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ce920628-85e6-443e-890c-d8ce03e84daa · outbound

This paper cites The predictron: End-to-end learning and planning.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model The predictron: End-to-end learning and planning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.797657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 88b4b50d-25e0-41f4-af69-c6033217d329 · outbound

This paper cites Sutton and Andrew G.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Sutton and Andrew G

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.800372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6aa88cb8-6df8-4dc5-8db5-7ad9ac7c6a84 · outbound

This paper cites Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.803079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fdbfe59d-acea-4252-9dba-8c4faaebfc65 · outbound

This paper cites Value iteration networks.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Value iteration networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.805511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ba69073c-6c0c-4b2d-b316-1b3a46b49117 · outbound

This paper cites When to use parametric models in reinforcement learning?.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model When to use parametric models in reinforcement learning?

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:57:02.691186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a0e4ff10-e316-44b4-bf1f-53a703adff2b · outbound

This paper cites Grandmaster level in StarCraft II using multi-agent reinforcement learning.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Grandmaster level in StarCraft II using multi-agent reinforcement learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.808671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6be1ff4f-c805-41c7-9ecc-88bb65c7ec96 · outbound

This paper cites Planning and scheduling.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Planning and scheduling

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.811663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 99b68e8a-8573-463b-ae42-3f0d3217ff85 · outbound

This paper cites From Pixels to Torques: Policy Learning with Deep Dynamical Models.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model From Pixels to Torques: Policy Learning with Deep Dynamical Models

Reference 48

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T23:57:02.704498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation db0ae163-1312-41d9-92ce-a0188abda6ed · outbound

This paper cites Embed to control: A locally linear latent dynamics model for control from raw images.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Embed to control: A locally linear latent dynamics model for control from raw images

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.814933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 034fcf6d-ef96-4f2e-9c17-1bcfa64e37ae · outbound

This paper cites AlphaZero had access to a perfect simulator of the true dynamics process.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model AlphaZero had access to a perfect simulator of the true dynamics process

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.819150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 399d8136-256a-4f17-bb8a-3fdec9a99a03 · outbound

This paper cites AlphaZero used the set of legal actions obtained from the simulator to mask the prior produced by the network everywhere in the search tree.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model AlphaZero used the set of legal actions obtained from the simulator to mask the prior produced by the network everywhere in the search tree

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.822130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b9717457-22f7-4b3f-a202-8c568cb7e7be · outbound

This paper cites AlphaZero stopped the search at tree nodes representing terminal states and used the ter- minal value provided by the simulator instead of the value produced by the network.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model AlphaZero stopped the search at tree nodes representing terminal states and used the ter- minal value provided by the simulator instead of the value produced by the network

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:57:02.825072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 50d9ab1e-4ae5-4369-89af-1eaed46cba74 · outbound

This paper cites In the experiments reported in this paper, we always unroll for K = 5 steps.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model In the experiments reported in this paper, we always unroll for K = 5 steps

Reference 53

Resolution: malformed identifier; raw_fallback, observed 2026-05-16T23:57:02.827329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 740144c2-903b-4025-b819-b4a2f3099d1f · inbound

Dream to Control: Learning Behaviors by Latent Imagination cites this paper.

Dream to Control: Learning Behaviors by Latent Imagination Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8ef18542-cc46-45cc-bd32-aa5d08957d03 · inbound

Mastering Atari with Discrete World Models cites this paper.

Mastering Atari with Discrete World Models Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b32641dd-c370-4285-bfd6-b12be9f104dd · inbound

Is Conditional Generative Modeling all you need for Decision-Making? cites this paper.

Is Conditional Generative Modeling all you need for Decision-Making? Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 130

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cd5bdfc2-fab3-4c3a-87e3-07c7e4759dd1 · inbound

Mastering Diverse Domains through World Models cites this paper.

Mastering Diverse Domains through World Models Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 836a8968-455d-4585-912e-99970718225f · inbound

Equivariant Action Sampling for Reinforcement Learning and Planning cites this paper.

Equivariant Action Sampling for Reinforcement Learning and Planning Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T14:28:18.643656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e325a2f-7316-4ab6-bb5b-e88de0f04010 · inbound

Hadamax Encoding: Elevating Performance in Model-Free Atari cites this paper.

Hadamax Encoding: Elevating Performance in Model-Free Atari Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T15:23:19.096967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0bd6c8f-4511-4359-a094-7cf928d285bc · inbound

Search-Based Multi-Trajectory Refinement for Safe C-to-Rust Translation with Large Language Models cites this paper.

Search-Based Multi-Trajectory Refinement for Safe C-to-Rust Translation with Large Language Models Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 23

Resolution: metadata mismatch; local_arxiv, observed 2026-05-22T14:36:40.902504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 153600d2-89b4-48e2-b783-c3a6f7d4ff8b · inbound

Path Channels and Plan Extension Kernels: a Mechanistic Description of Planning in a Sokoban RNN cites this paper.

Path Channels and Plan Extension Kernels: a Mechanistic Description of Planning in a Sokoban RNN Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T04:42:27.946692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 325612bb-76e1-4fee-ba98-6d6b5f4dd786 · inbound

The Serial Scaling Hypothesis cites this paper.

The Serial Scaling Hypothesis Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 97

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:12:02.429713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 37899805-d3a8-4eca-9aa3-9f73fabb337d · inbound

Evolutionary Optimization of Deep Learning Agents for Sparrow Mahjong cites this paper.

Evolutionary Optimization of Deep Learning Agents for Sparrow Mahjong Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T22:06:19.678869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd4a96a0-65d4-4244-a14b-056c4870268d · inbound

TransZero: Parallel Tree Expansion in MuZero using Transformer Networks cites this paper.

TransZero: Parallel Tree Expansion in MuZero using Transformer Networks Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T16:58:12.980404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6ea2bd0-f48a-4b74-8cd9-18e3aa597b52 · inbound

Training Agents Inside of Scalable World Models cites this paper.

Training Agents Inside of Scalable World Models Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0aff5ca6-ef4a-4541-9ccd-b8f3539a0589 · inbound

Latent Chain-of-Thought World Modeling for End-to-End Driving cites this paper.

Latent Chain-of-Thought World Modeling for End-to-End Driving Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 32e42319-a270-48e8-aa0a-0d3aaf776b89 · inbound

Variance-Aware Prior-Based Tree Policies for Monte Carlo Tree Search cites this paper.

Variance-Aware Prior-Based Tree Policies for Monte Carlo Tree Search Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 3

Resolution: verified exact; doi, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aac7798c-3ff3-49f5-88da-6695b44cfdbf · inbound

What Drives Success in Physical Planning with Joint-Embedding Predictive World Models? cites this paper.

What Drives Success in Physical Planning with Joint-Embedding Predictive World Models? Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 57

Resolution: verified exact; doi, observed 2026-05-21T15:34:14.849799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6dfb7745-6c8e-413a-99b2-f91f3d872264 · 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 Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 47

Resolution: verified exact; doi, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5a43d936-5686-48d8-8d95-f96df85c58b1 · inbound

Reproducibility study on how to find Spurious Correlations, Shortcut Learning, Clever Hans or Group-Distributional non-robustness and how to fix them cites this paper.

Reproducibility study on how to find Spurious Correlations, Shortcut Learning, Clever Hans or Group-Distributional non-robustness and how to fix them Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 3

Resolution: metadata mismatch; doi, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9c0cc5bc-6019-46f6-ae9b-395aff5c13fa · inbound

Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models cites this paper.

Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 12

Resolution: metadata mismatch; doi, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6a694690-95e8-4a5b-8768-08771c91c5f7 · inbound

Interpretable experiential learning based on state history and global feedback cites this paper.

Interpretable experiential learning based on state history and global feedback Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 26

Resolution: verified exact; doi, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 238997d7-fd19-4156-86ab-cfa807ed87d0 · inbound

Latent State Design for World Models under Sufficiency Constraints cites this paper.

Latent State Design for World Models under Sufficiency Constraints Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5d042b98-0993-4efa-b50e-99f2f9e47f23 · inbound

Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits cites this paper.

Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 3

Resolution: verified exact; doi, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6287f7af-0b07-461c-a6d8-62c7989292d4 · inbound

Beyond the Independence Assumption: Finite-Sample Guarantees for Deep Q-Learning under $\tau$-Mixing cites this paper.

Beyond the Independence Assumption: Finite-Sample Guarantees for Deep Q-Learning under $\tau$-Mixing Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 60

Resolution: verified exact; doi, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e4ecb6f7-e826-4767-b0f2-a8a6f0798d33 · inbound

PMCTS: Particle Monte Carlo Tree Search for Principled Parallelized Inference Time Scaling cites this paper.

PMCTS: Particle Monte Carlo Tree Search for Principled Parallelized Inference Time Scaling Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 4

Resolution: verified exact; doi, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 00320184-a98e-453d-ac6a-d884111b680d · inbound

PMCTS: Particle Monte Carlo Tree Search for Principled Parallelized Inference Time Scaling cites this paper.

PMCTS: Particle Monte Carlo Tree Search for Principled Parallelized Inference Time Scaling Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 4

Resolution: verified exact; doi, observed 2026-05-22T09:54:46.063301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a95139fb-0756-464c-9da7-548599e45f03 · inbound

Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning cites this paper.

Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 35

Resolution: verified exact; doi, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2c775e6a-d6d8-4b07-a121-221c4d73152c · inbound

Plan Before You Trade: Inference-Time Optimization for RL Trading Agents cites this paper.

Plan Before You Trade: Inference-Time Optimization for RL Trading Agents Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 9

Resolution: verified exact; doi, observed 2026-05-16T23:57:02.849350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8a29e827-628e-43af-b350-c8f21629d059 · inbound

TFGN: Task-Free, Replay-Free Continual Pre-Training Without Catastrophic Forgetting at LLM Scale cites this paper.

TFGN: Task-Free, Replay-Free Continual Pre-Training Without Catastrophic Forgetting at LLM Scale Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-05-19T17:07:41.583577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5b7addc5-168b-4dfe-80f3-dd04510a01ec · inbound

HalluWorld: A Controlled Benchmark for Hallucination via Reference World Models cites this paper.

HalluWorld: A Controlled Benchmark for Hallucination via Reference World Models Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 55

Resolution: verified exact; doi, observed 2026-05-20T05:53:04.201148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3ec4a674-5653-4680-8bbe-abac63264efa · inbound

ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders cites this paper.

ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 26

Resolution: metadata mismatch; doi, observed 2026-05-20T05:33:03.812197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b6832880-bbe5-437d-821d-7eb5b780736e · inbound

ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders cites this paper.

ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 26

Resolution: metadata mismatch; doi, observed 2026-05-21T07:39:48.420072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d64ab1db-89d8-4758-afb4-4190cdcb4eb8 · inbound

Neuro-Inspired Inverse Learning for Planning and Control cites this paper.

Neuro-Inspired Inverse Learning for Planning and Control Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 39

Resolution: metadata mismatch; doi, observed 2026-06-30T16:14:52.778744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 55b995ea-83ab-4955-912e-1ce083567e06 · inbound

ECHO: Terminal Agents Learn World Models for Free cites this paper.

ECHO: Terminal Agents Learn World Models for Free Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 13

Resolution: metadata mismatch; doi, observed 2026-06-30T15:04:46.066623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4460d82b-8089-4b4a-9bac-c3b7c43e1e7c · inbound

MiraBench: Evaluating Action-Conditioned Reliability in Robotic World Models cites this paper.

MiraBench: Evaluating Action-Conditioned Reliability in Robotic World Models Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 34

Resolution: verified exact; doi, observed 2026-06-29T07:53:13.179300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 563cd976-d1fa-4cea-8fcd-9e795712a046 · inbound

Physically Viable World Models: A Case for Query-Conditioned Embodied AI cites this paper.

Physically Viable World Models: A Case for Query-Conditioned Embodied AI Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-06-29T09:13:16.528091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation de0c8f94-cba7-4ac8-b9a7-7dc7a5a179f1 · inbound

GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization cites this paper.

GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 33

Resolution: verified exact; doi, observed 2026-06-28T23:12:46.188973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 827cb0a4-d083-4405-bc59-b0d277d43980 · inbound

Can Predicted Dynamics Exist in the Physical World? cites this paper.

Can Predicted Dynamics Exist in the Physical World? Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 27

Resolution: verified exact; doi, observed 2026-06-30T13:04:39.065722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3df8379d-fe57-4761-95b1-3d3990d6571b · inbound

Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems cites this paper.

Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 98

Resolution: verified exact; doi, observed 2026-06-30T13:04:39.459819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d5d48e0c-370d-4108-89e3-9ab9e4922e10 · inbound

Robots Need More than VLA and World Models cites this paper.

Robots Need More than VLA and World Models Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 128

Resolution: metadata mismatch; doi, observed 2026-06-28T01:11:28.893372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5c28508f-2868-49f4-9f4e-0609fa3cb50c · inbound

Bridging the Agent-World Gap: Text World Models for LLM-based Agents cites this paper.

Bridging the Agent-World Gap: Text World Models for LLM-based Agents Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 9

Resolution: verified exact; doi, observed 2026-06-27T17:11:05.581961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d787f317-1077-4bdf-ae82-93315e9aa765 · inbound

Business World Model cites this paper.

Business World Model Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 6

Resolution: verified exact; doi, observed 2026-06-30T10:34:36.063813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 147385d6-71f9-4d0a-893e-3df221224863 · inbound

ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience cites this paper.

ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 10

Resolution: verified exact; doi, observed 2026-06-27T13:40:57.247355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d8050637-4a82-427b-af3b-d87f50a4ee5e · inbound

A Tutorial on World Models and Physical AI cites this paper.

A Tutorial on World Models and Physical AI Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 36

Resolution: verified exact; doi, observed 2026-06-27T07:30:41.015511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 90acd6ed-15b2-4554-9134-e0b26bcb4e54 · inbound

Looped World Models cites this paper.

Looped World Models Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 21

Resolution: metadata mismatch; doi, observed 2026-06-27T01:50:21.207727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d762848a-12b6-45b7-b3ca-c60cffc899a8 · inbound

NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning cites this paper.

NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 10

Resolution: metadata mismatch; doi, observed 2026-06-26T14:49:32.413823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 04c1852d-b0c8-4448-ae86-153ac9bc8b40 · inbound

Self-Evolving Cognitive Framework via Causal World Modeling for Embodied Scientific Intelligence cites this paper.

Self-Evolving Cognitive Framework via Causal World Modeling for Embodied Scientific Intelligence Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 22

Resolution: verified exact; doi, observed 2026-06-26T11:09:23.329390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 175f8e93-d004-4694-8b71-bc36b2604629 · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 183

Resolution: verified exact; local_arxiv, observed 2026-07-04T11:09:46.258896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8dd53e14-6c8d-49b8-b648-046595ee24f9 · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 171

Resolution: unresolved; no resolver link, observed 2026-08-02T10:27:18.390194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc5a233e-a12e-4b3c-84da-e362d1963ef2 · inbound

A Definition and Roadmap for World Models cites this paper.

A Definition and Roadmap for World Models Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 65

Resolution: verified exact; doi, observed 2026-07-08T07:14:44.355812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2a000284-4c95-41bb-9b52-0072ed5e25ff · inbound

The Rank-One Corner: How Much Value Equivalence Does a Task Need from a World Model? cites this paper.

The Rank-One Corner: How Much Value Equivalence Does a Task Need from a World Model? Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 8

Resolution: verified exact; doi, observed 2026-07-11T00:47:42.015170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b782cc45-4b09-48b5-b09a-6a3f84248914 · inbound

Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification cites this paper.

Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T09:55:54.826178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27dd0e8d-7c85-4c6b-a960-06d8785d7087 · inbound

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills cites this paper.

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 220

Resolution: unresolved; no resolver link, observed 2026-08-04T19:45:35.186511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a9b3376-327b-4d55-a4d5-7c7bf8780bd1 · inbound

Quo Vadis, World Modeling? cites this paper.

Quo Vadis, World Modeling? Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Reference 136

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:10.585654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.