Paper Citation Record · LEDGER

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal

As of 20 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2607.18296.

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

pith.paper-citation-record.v1
2607.18296 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:01:03.535066Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

verified exact0
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4e03f3c-509b-43d9-a625-c59a7173940a · outbound

This paper cites Baghchal: An augmented Q-learning approach to turn-based heterogeneous multi- agent systems,.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Baghchal: An augmented Q-learning approach to turn-based heterogeneous multi- agent systems,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.169475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb7b9925-d60f-4198-b1b5-bb12294043a1 · outbound

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

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Human-level control through deep reinforcement learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.256634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56e1f317-fc36-4134-96e2-3102e50fea8a · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning,.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Simple statistical gradient-following algorithms for connectionist reinforcement learning,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.408707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 965f6a8d-d1ee-441a-a2f9-a9e610c5161e · outbound

This paper cites Proximal Policy Optimization Algorithms.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Proximal Policy Optimization Algorithms

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.483968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30654ff6-58d7-4413-92de-83b31636efea · outbound

This paper cites MasteringAtari,Go,chessandshogibyplanningwith alearnedmodel,.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal MasteringAtari,Go,chessandshogibyplanningwith alearnedmodel,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.489475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66dd54fe-b38f-4040-b336-7238dcea4e90 · outbound

This paper cites Mastering the game of Go with deep neural networks and treesearch,.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Mastering the game of Go with deep neural networks and treesearch,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.493918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54404250-2567-450e-b218-4deaf81ac426 · outbound

This paper cites an unresolved cited work.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.499141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28ae733c-5f2f-4285-93f8-7648c1a7625b · outbound

This paper cites Superhuman AI for multiplayer poker,.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Superhuman AI for multiplayer poker,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.503169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ed4ef66-14e5-4a09-adf2-8bbb11385577 · outbound

This paper cites Hindsight Experience Replay.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Hindsight Experience Replay

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.507472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e5b21c0-fd39-4720-9d6d-5d07b6f3e568 · outbound

This paper cites Counterfactual multi-agent policy gradients,.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Counterfactual multi-agent policy gradients,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.512299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89e3c950-78de-4a04-a9cd-0db7cb6a13c9 · outbound

This paper cites A unified game-theoretic approach to multiagent reinforcement learning,.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal A unified game-theoretic approach to multiagent reinforcement learning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.516477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81a406b0-0086-41ba-886f-8ea38382650a · outbound

This paper cites Geometric phase predicts locomotion performance in undulating living systems across scales.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Geometric phase predicts locomotion performance in undulating living systems across scales

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.521396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1ee4d99-8911-4051-ad51-a6510ab2b416 · outbound

This paper cites Deep Blue,.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Deep Blue,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.526243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d52b22d-868a-4dd0-8169-d4af2b2b0829 · outbound

This paper cites Dota 2 with Large Scale Deep Reinforcement Learning.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal Dota 2 with Large Scale Deep Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.530354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfa83d99-ec54-4cfa-9caf-4fb3e24fed20 · outbound

This paper cites AIstrategyapproachdevelopment on Baghchal using AlphaZero,.

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal AIstrategyapproachdevelopment on Baghchal using AlphaZero,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:03.535066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.