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Paper Citation Record · LEDGER

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning

As of 11 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2501.10938.

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

pith.paper-citation-record.v1
2501.10938 v1

Coverage vector

measured 53 of 53 reference resolution

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measured 53 of 53 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

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Outbound references

Observation 1b36eabe-b713-473a-b152-9f742ef26701 · outbound

This paper cites A reinforcement learning method for human-robot collaboration in assembly tasks,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning A reinforcement learning method for human-robot collaboration in assembly tasks,

Reference 1

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This paper cites IoT sensor selection for target localization: A reinforcement learning based approach,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning IoT sensor selection for target localization: A reinforcement learning based approach,

Reference 2

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This paper cites Self- supervised online and light-weight anomaly and event detection for iot devices,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Self- supervised online and light-weight anomaly and event detection for iot devices,

Reference 3

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

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Mastering the game of go without human knowledge,

Reference 4

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Observation 713fb8e3-3f70-4bf2-b1b8-f6436955e3a9 · outbound

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

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Dota 2 with Large Scale Deep Reinforcement Learning

Reference 5

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Observation 4f40ba40-dca8-41b5-a0ac-01049c1bd02a · outbound

This paper cites Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrow’s inter- sections,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrow’s inter- sections,

Reference 6

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Observation 9f1b34a7-47c5-42e7-9aaa-47c1c1496ed4 · outbound

This paper cites Target lo- calization using multi-agent deep reinforcement learning with proximal policy optimization,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Target lo- calization using multi-agent deep reinforcement learning with proximal policy optimization,

Reference 7

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This paper cites Fault- tolerant federated reinforcement learning with theoretical guarantee,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Fault- tolerant federated reinforcement learning with theoretical guarantee,

Reference 8

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This paper cites Resource allocation in iot edge computing via concurrent federated reinforcement learning,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Resource allocation in iot edge computing via concurrent federated reinforcement learning,

Reference 9

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Observation 1c3d613e-ffa2-4084-b849-03d26e0fd8bb · outbound

This paper cites Federated reinforcement learn- ing for fast personalization,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Federated reinforcement learn- ing for fast personalization,

Reference 10

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Observation 5f636dd2-1368-46e4-8a63-a2302e8f6942 · outbound

This paper cites Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,

Reference 11

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Observation 9f09f157-6264-44c4-b2c0-d4272e4a8f12 · outbound

This paper cites Learning to utilize shaping rewards: A new approach of reward shaping,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Learning to utilize shaping rewards: A new approach of reward shaping,

Reference 12

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Observation abcf40a3-55e0-45a8-ae1d-8d96b4a48e39 · outbound

This paper cites Graph convolutional recurrent networks for reward shaping in reinforcement learning,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Graph convolutional recurrent networks for reward shaping in reinforcement learning,

Reference 13

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Observation 0dacecef-2c40-46aa-9e6f-94008007921d · outbound

This paper cites Reward Shaping Using Convolutional Neural Network.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Reward Shaping Using Convolutional Neural Network

Reference 14

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Observation 87332c17-9d06-4394-ac23-fdb50d036692 · outbound

This paper cites Federated learning for internet of things: A comprehensive survey,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Federated learning for internet of things: A comprehensive survey,

Reference 15

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Observation 6cee09f1-8dd1-4a7b-afc3-80657def30f2 · outbound

This paper cites Knowledge distillation: A survey,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Knowledge distillation: A survey,

Reference 16

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Observation 2e3eef1e-6bd8-49b4-add6-b890f1b7c7b4 · outbound

This paper cites Federated Reinforcement Learning: Techniques, Applications, and Open Challenges.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 17

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Observation 2e819415-e1df-4440-ac19-07042cd00a5d · outbound

This paper cites Overcoming exploration in reinforcement learning with demonstrations,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Overcoming exploration in reinforcement learning with demonstrations,

Reference 18

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Observation 49ffafec-7dd3-4c35-9776-5e0d80eaf403 · outbound

This paper cites Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Reference 19

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Observation 75b54225-ec3d-46e0-9e0f-5d7eeafecbf6 · outbound

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Federated learning meets blockchain in edge computing: Opportunities and challenges,

Reference 20

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Observation 64cc76fc-eb81-4d71-9092-47e6931f6960 · outbound

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning A context-aware blockchain-based crowdsourcing framework: Open challenges and op- portunities,

Reference 21

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Observation 89093bfd-5ab0-4232-b90a-2bb2cdfb7240 · outbound

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Federated multiagent actor– critic learning for age sensitive mobile-edge computing,

Reference 22

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning,

Reference 23

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Observation 84603e51-9e1d-451a-883e-0051d11be0f0 · outbound

This paper cites When deep rein- forcement learning meets federated learning: Intelligent multitimescale resource management for multiaccess edge computing in 5g ultradense network,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning When deep rein- forcement learning meets federated learning: Intelligent multitimescale resource management for multiaccess edge computing in 5g ultradense network,

Reference 24

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Observation 333c33f0-e8be-4544-a456-6170a51ef34f · outbound

This paper cites Lifelong federated reinforcement learn- ing: a learning architecture for navigation in cloud robotic systems,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Lifelong federated reinforcement learn- ing: a learning architecture for navigation in cloud robotic systems,

Reference 25

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Observation 01bae6c8-747f-4b10-a295-7b787fe6a43a · outbound

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Federated transfer reinforcement learning for autonomous driving,

Reference 26

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Double Q-learning for radiation source detection,

Reference 27

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Multi- agent deep reinforcement learning with demonstration cloning for target localization,

Reference 28

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Emergent tool use from multi-agent autocurricula,

Reference 29

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Principled reward shaping for rein- forcement learning via lyapunov stability theory,

Reference 30

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Primal: Pathfinding via reinforcement and imitation multi- agent learning,

Reference 31

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Primal 2: Pathfind- ing via reinforcement and imitation multi-agent learning-lifelong,

Reference 32

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Multi-agent deep reinforcement learning: a survey,

Reference 33

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 34

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Observation 6afa10c5-0bca-47bc-abc2-05b9aa6e7d47 · outbound

This paper cites High- dimensional continuous control using generalized advantage estimation,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning High- dimensional continuous control using generalized advantage estimation,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.650794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.142508Z digest=sha256:4e99523868354d2f11f764403d138fba40f5cc3d3ab762a31a83ff1f554c3792

Observation 203277c9-6b80-4c61-8689-633d8481f284 · outbound

This paper cites Roulette-wheel selection via stochastic acceptance,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Roulette-wheel selection via stochastic acceptance,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.634122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.147059Z digest=sha256:99f03f81591c0258796348576795e821906e958139db900cfb1a31f4c242afab

Observation a0e0f091-cc78-46fa-95dc-93724b49187d · outbound

This paper cites Influence-and interest- based worker recruitment in crowdsourcing using online social net- works,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Influence-and interest- based worker recruitment in crowdsourcing using online social net- works,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.619133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.151682Z digest=sha256:12d752f2639c9be80451ae34447fa76521c98ddd96f61eb9af4e1f666fa074f4

Observation 7ef7c5fe-5936-4faa-9999-a6c98522c20c · outbound

This paper cites On- chain behavior prediction machine learning model for blockchain-based 14 crowdsourcing,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning On- chain behavior prediction machine learning model for blockchain-based 14 crowdsourcing,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.603783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.156708Z digest=sha256:664a83aee85674ebe711dbd1f3b2b00987e437d9b81bf395df02d74909c98dbb

Observation 25a3a1ec-5c9b-4d84-ace9-077da33274b5 · outbound

This paper cites IPFS - Content Addressed, Versioned, P2P File System.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning IPFS - Content Addressed, Versioned, P2P File System

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-10T18:54:19.161376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:54:19.161376Z digest=sha256:67d5bc0eb2c1f2f5e89af40f20aac71203d8f8df51c3cb4a0b806eb966f9f36f

Observation 3dac2287-c7b9-48ba-b0a5-2ae5018f6a58 · outbound

This paper cites An optimization and auction-based incentive mechanism to maximize social welfare for mobile crowdsourcing,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning An optimization and auction-based incentive mechanism to maximize social welfare for mobile crowdsourcing,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.587956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.166316Z digest=sha256:3723b539fa939f41ceb9bcf87b9556bebd37f5ecb74441b9edb6092c51ee36e4

Observation dcf82a82-0968-4408-89b4-951a9d354ad9 · outbound

This paper cites A worker-selection incentive mechanism for optimizing platform-centric mobile crowdsourcing sys- tems,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning A worker-selection incentive mechanism for optimizing platform-centric mobile crowdsourcing sys- tems,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.572389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.170880Z digest=sha256:73f471a82d6692265a1842d72bf75a5d7a105d82f5e5f44fcb8f02484f848c18

Observation 8d1aadb1-45c7-4140-85cb-98bc59c70dff · outbound

This paper cites Auction fever: Rising revenue in second-price auction formats,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Auction fever: Rising revenue in second-price auction formats,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.556907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.175427Z digest=sha256:4dee0bbf6c52e72ae133cd51dc2ab02e6068d4e21e44ead4c026c3e90ca88d5d

Observation baf3eb19-fa66-4c0e-b766-c0477e1e65e4 · outbound

This paper cites Path planning and scheduling for a fleet of autonomous vehicles,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Path planning and scheduling for a fleet of autonomous vehicles,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.541367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.180100Z digest=sha256:3698898e63ac1832a332956885470b7ff9eea73c235bec28802c99a525fd6ec4

Observation d36e07c8-173e-4559-b7e0-245ae5563799 · outbound

This paper cites Autonomous vehicle fleet coordination with deep reinforce- ment learning,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Autonomous vehicle fleet coordination with deep reinforce- ment learning,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.524954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.184827Z digest=sha256:25395c9a6b3c83d01aab68414f32cf248efbf5ab1cfc33ffeb914f436578e11c

Observation e7411301-cd24-4f55-8dff-89f4059d99f7 · outbound

This paper cites Multi-agent reinforcement learning with di- rected exploration and selective memory reuse,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Multi-agent reinforcement learning with di- rected exploration and selective memory reuse,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.508189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.189811Z digest=sha256:c0c7889bf64c97d388105738300c2869009937418a3e279e521abe90817db4b8

Observation 54bc74fb-09dc-4983-ba4b-b025b353f818 · outbound

This paper cites Data-driven dynamic active node selection for event localization in IoT applications-a case study of radiation localization,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Data-driven dynamic active node selection for event localization in IoT applications-a case study of radiation localization,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.492301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.194537Z digest=sha256:1c2c0c461b841bff8f76597ca66776cba522ba760f37acf5fdccae033aa9fecd

Observation 6169b1ea-8a36-41dc-bac5-1a1d70eb9666 · outbound

This paper cites Reinforcement learn- ing framework for uav-based target localization applications,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Reinforcement learn- ing framework for uav-based target localization applications,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.476036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.199215Z digest=sha256:b73257831c6a4d20a587bb5dbd44afbd3982ca5eb7d4a0d188c2afac45af3256

Observation 154a7028-4964-4d4e-b027-b3e51978723b · outbound

This paper cites A predictive target tracking framework for iot using cnn–lstm,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning A predictive target tracking framework for iot using cnn–lstm,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.459449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.204055Z digest=sha256:20f53eb30653889cf51e5add9ece064a7eb306a5e2907023463bf4d0f58e7fef

Observation fe7d37f9-527a-4b16-acfc-20f125bc31ff · outbound

This paper cites RFLS-resilient fault- proof localization system in IoT and crowd-based sensing applications,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning RFLS-resilient fault- proof localization system in IoT and crowd-based sensing applications,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.441035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.208581Z digest=sha256:a62881ebaa80cca5296f1417df97e20577edeb57412628f46bab02ce63ed44d2

Observation f3954c10-3e49-4e6a-86bb-7c335cbbfb93 · outbound

This paper cites SDRS: A stable data-based recruitment system in IoT crowdsensing for localiza- tion tasks,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning SDRS: A stable data-based recruitment system in IoT crowdsensing for localiza- tion tasks,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.424868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.213049Z digest=sha256:7f5dd686a14be23844d3dd0263dfa83ed1d75129c0cbcecaf06c64c453fdaaf5

Observation fafdc694-aa35-45ce-b5d1-81f1ed07bd5e · outbound

This paper cites A uav- assisted search and localization strategy in non-line-of-sight scenarios,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning A uav- assisted search and localization strategy in non-line-of-sight scenarios,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.408887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.217885Z digest=sha256:0c162121ed52f43632f5fd2408401168657398aec2333e019e915b1e47748709

Observation e3f0d804-a321-4884-a6b0-94382ca5ef84 · outbound

This paper cites A matching game-based crowdsourcing framework for last-mile delivery: Ground-vehicles and unmanned-aerial vehicles,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning A matching game-based crowdsourcing framework for last-mile delivery: Ground-vehicles and unmanned-aerial vehicles,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.392639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.222608Z digest=sha256:36d3580bacce5fd0dfa9539b0ba38b8179aac991154c309be4b44bf7ef8a4da6

Observation e665bd22-862b-4479-9081-3a6e27cc0ed0 · outbound

This paper cites Lenet-5, convolutional neural networks,.

Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning Lenet-5, convolutional neural networks,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:54:19.376243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T18:54:19.227490Z digest=sha256:e5a228c9d5e38ff4f1666b0d64aa3a31849a1173af99de7587807687d801f80e

Pith citing papers

No inbound Pith citation observations are available.