Paper Citation Record · LEDGER

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems

As of 7 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2607.10309.

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

pith.paper-citation-record.v1
2607.10309 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T12:45:19.511401Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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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

46 of 46 outbound references displayed

verified exact7
verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 20b0860d-126a-482f-8fc4-c86a73523e2d · outbound

This paper cites an unresolved cited work.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Unresolved cited work

Reference 1

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This paper cites Goodfellow, Y.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Goodfellow, Y

Reference 2

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Learning dexterous in-hand manipulation,

Reference 3

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Scalable deep reinforcement learning for vision- Based robotic manipulation,

Reference 4

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

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play,

Reference 5

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Observation 925197f2-18a4-4c82-a4e3-5eee62175b80 · outbound

This paper cites Training language models to follow instructions with human feedback,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Training language models to follow instructions with human feedback,

Reference 6

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Observation 78105a42-c6d1-416a-816c-bc85a157bff8 · outbound

This paper cites Magnetic control of tokamak plasmas through deep reinforcement learning,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Magnetic control of tokamak plasmas through deep reinforcement learning,

Reference 7

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Observation 3459a4a5-e4da-4b8c-8535-fd2c087d652e · outbound

This paper cites That ‘internet of things’ thing,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems That ‘internet of things’ thing,

Reference 8

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This paper cites Autonomous IoT in a Few Words,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Autonomous IoT in a Few Words,

Reference 9

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This paper cites Edge Computing: Vision and Challenges,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Edge Computing: Vision and Challenges,

Reference 10

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Observation fe913362-89c9-4303-b9b8-5982d8aec6c8 · outbound

This paper cites Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,

Reference 11

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Observation 397c4774-3938-4096-9375-694592e98d3c · outbound

This paper cites Deep Reinforcement Learning for Autonomous Internet of Things: Model, Ap- plications and Challenges,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Deep Reinforcement Learning for Autonomous Internet of Things: Model, Ap- plications and Challenges,

Reference 12

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Human-level control through deep reinforcement learning,

Reference 13

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This paper cites Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents,

Reference 14

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This paper cites Data center cooling using model-predictive con- trol,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Data center cooling using model-predictive con- trol,

Reference 15

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Observation 74ece2d2-92f2-4aaa-bc9f-5f29c2196bb4 · outbound

This paper cites Exploring Deep Reinforcement Learn- ing for Holistic Smart Building Control,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Exploring Deep Reinforcement Learn- ing for Holistic Smart Building Control,

Reference 16

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Observation ebf2f86c-6a4d-421d-b0b5-3a654a702f31 · outbound

This paper cites Broad Reinforcement Learning for Supporting Fast Autonomous IoT,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Broad Reinforcement Learning for Supporting Fast Autonomous IoT,

Reference 17

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Observation b05c189f-06d6-4237-b22d-bac23fb03616 · outbound

This paper cites River Flow Path Control With Reinforcement Learning,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems River Flow Path Control With Reinforcement Learning,

Reference 18

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Observation 5acdf35a-f229-4dc7-b1b4-9aa4215db94e · outbound

This paper cites A Reinforcement Learning Empowered Cooperative Control Approach for IIoT-Based Virtually Coupled Train Sets,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems A Reinforcement Learning Empowered Cooperative Control Approach for IIoT-Based Virtually Coupled Train Sets,

Reference 19

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Observation 5fe87b9c-b57e-41b9-877a-7abba364ddd8 · outbound

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Deep Reinforcement Learning for Smart Home Energy Management,

Reference 20

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Observation fd0af617-20c3-4924-aa83-e0438444ca55 · outbound

This paper cites Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey,.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey,

Reference 21

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Observation d26bf51a-8bba-4177-ac52-8d258a8eacf5 · outbound

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems OpenAI Gym

Reference 22

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Observation 4faef6c3-a9c0-4ec3-b2a5-7f32082c7536 · outbound

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 23

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems The arcade learning environment: An evaluation platform for general agents,

Reference 24

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems CALE: Continuous Arcade Learning Environment,

Reference 25

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This paper cites Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots

Reference 26

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems MuJoCo: A physics engine for model-based control,

Reference 27

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Open-sourcing MuJoCo

Reference 28

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Zakka, Y

Reference 29

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Continuous control with deep reinforcement learning

Reference 30

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems PyBullet, a Python module for physics simu- lation for games, robotics and machine learning

Reference 31

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments,

Reference 32

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 33

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems RLBench: The Robot Learning Benchmark & Learning Environment,

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Over- coming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL,

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Flappy Bird,

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems A3C Keras FlappyBird,

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Breakout,

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Teensy 4.1 Development Board

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems etherkey

Reference 40

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Ob- servations on Typing from 136 Million Keystrokes,

Reference 41

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Deep Reinforcement Learning at the Edge of the Statistical Precipice,

Reference 42

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems real world program,

Reference 43

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Stable-Baselines3: Reliable Reinforcement Learning Imple- mentations,

Reference 44

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Asynchronous Methods for Deep Reinforcement Learn- ing,

Reference 45

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Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Proximal Policy Optimization Algorithms

Reference 46

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Pith citing papers

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