Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning | Paper Citation Record

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This paper cites Playing Atari with Deep Reinforcement Learning.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 1

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

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play,

Reference 2

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This paper cites Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,

Reference 3

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This paper cites Learning dexterous in-hand manipu- lation,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Learning dexterous in-hand manipu- lation,

Reference 4

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Learning to drive in a day,

Reference 5

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

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Magnetic control of tokamak plasmas through deep reinforcement learning,

Reference 6

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This paper cites Sample-efficient reinforcement learning for CERN accelerator control,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Sample-efficient reinforcement learning for CERN accelerator control,

Reference 7

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Observation 3644a469-9450-41b9-923a-d2038a6ccc7a · outbound

This paper cites Reinforcement learning for charged-particle tracking Reinforcement learning,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Reinforcement learning for charged-particle tracking Reinforcement learning,

Reference 8

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This paper cites Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters with Reinforcement Learning,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters with Reinforcement Learning,

Reference 9

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Unresolved cited work

Reference 10

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Observation e9203ae9-99e1-4290-bac9-4216ef8bbf6b · outbound

This paper cites Markov games as a framework for multi-agent rein- forcement learning,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Markov games as a framework for multi-agent rein- forcement learning,

Reference 11

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This paper cites Learning tsp requires rethinking generalization,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Learning tsp requires rethinking generalization,

Reference 12

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This paper cites Differentiation of Blackbox Combinatorial Solvers,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Differentiation of Blackbox Combinatorial Solvers,

Reference 13

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This paper cites Proton tracking algorithm in a pixel-based range telescope for proton computed tomography,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Proton tracking algorithm in a pixel-based range telescope for proton computed tomography,

Reference 14

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This paper cites Cliff diving: Exploring reward surfaces in reinforce- ment learning environments,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Cliff diving: Exploring reward surfaces in reinforce- ment learning environments,

Reference 15

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This paper cites Launch and iterate: Reducing prediction churn,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Launch and iterate: Reducing prediction churn,

Reference 16

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This paper cites A High-Granularity Digital Tracking Calorimeter Optimized for Proton CT,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning A High-Granularity Digital Tracking Calorimeter Optimized for Proton CT,

Reference 17

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning The bergen proton CT system,

Reference 18

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This paper cites ALPIDE, the Monolithic Active Pixel Sensor for the ALICE ITS upgrade,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning ALPIDE, the Monolithic Active Pixel Sensor for the ALICE ITS upgrade,

Reference 19

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning The ALPIDE pixel sensor chip for the upgrade of the ALICE Inner Tracking System,

Reference 20

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This paper cites Passage of particles through matter,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Passage of particles through matter,

Reference 21

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Radiotherapy Proton Interactions in Matter,

Reference 22

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This paper cites Application of Kalman filtering to track and vertex fitting,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Application of Kalman filtering to track and vertex fitting,

Reference 23

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This paper cites A concurrent track evolution algorithm for pattern recog- nition in the HERA-B main tracking system,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning A concurrent track evolution algorithm for pattern recog- nition in the HERA-B main tracking system,

Reference 24

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Tracking elementary particles near their primary vertex: A combinatorial approach,

Reference 25

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This paper cites Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data,

Reference 26

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning High Pileup Particle Tracking with Object Condensation

Reference 27

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This paper cites Charged particle tracking via edge-classifying interaction networks,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Charged particle tracking via edge-classifying interaction networks,

Reference 28

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This paper cites Exploring end-to-end differentiable neural charged particle tracking - a loss landscape perspective,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Exploring end-to-end differentiable neural charged particle tracking - a loss landscape perspective,

Reference 29

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World,

Reference 30

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Safe Exploration in Continuous Action Spaces

Reference 31

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces

Reference 32

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Safe Multi-Agent Reinforcement Learning via Shielding

Reference 33

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Safe Reinforcement Learning via Shielding

Reference 34

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Optimal and approximate Q-value functions for decentralized POMDPs,

Reference 35

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Policy iteration for decentralized control of markov decision processes,

Reference 36

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Some practical remarks on multiple scattering,

Reference 37

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Backpropagation through combinatorial algorithms: Identity with projection works,

Reference 38

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Pointer networks,

Reference 39

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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Neural machine translation by jointly learning to align and translate,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:25.149286Z

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This paper cites Noisy networks for exploration,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Noisy networks for exploration,

Reference 41

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This paper cites Parameter space noise for exploration,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Parameter space noise for exploration,

Reference 42

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Observation e7d8cc6a-48fd-4442-b641-5b211458ddfd · outbound

This paper cites Multi-Agent Reinforcement Learning: Independent vs. Cooper- ative Agents,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Multi-Agent Reinforcement Learning: Independent vs. Cooper- ative Agents,

Reference 43

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This paper cites Value-decomposition networks for cooperative multi- agent learning based on team reward,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Value-decomposition networks for cooperative multi- agent learning based on team reward,

Reference 44

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This paper cites Actor-attention-critic for multi-agent reinforcement learning,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Actor-attention-critic for multi-agent reinforcement learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:25.088796Z

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Observation 9fe9f518-5e61-4903-9ad2-35d0f78150fa · outbound

This paper cites Layer Normalization.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Layer Normalization

Reference 46

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This paper cites Deep residual learning for image recognition,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Deep residual learning for image recognition,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:25.074164Z

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Observation e46cddb1-2bb6-46be-8458-a9dec95a4095 · outbound

This paper cites On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T21:26:24.463573Z

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This paper cites The surprising effectiveness of ppo in cooperative multi- agent games,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning The surprising effectiveness of ppo in cooperative multi- agent games,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:25.056234Z

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Observation 151b1064-b5d8-42eb-8fdb-1ddb839981f9 · outbound

This paper cites Value-decomposition multi-agent proximal policy op- timization,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Value-decomposition multi-agent proximal policy op- timization,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:25.040467Z

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Observation c0b3e271-97f4-413f-b63b-d8bfc018b550 · outbound

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

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning High-dimensional continuous control using gener- alized advantage estimation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:25.025436Z

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Observation e58e0d17-87c2-4b9b-a0a9-b2ef6335849a · outbound

This paper cites Continuous control with deep reinforcement learning,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Continuous control with deep reinforcement learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:25.009893Z

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Observation 9487898f-f3b3-4250-befc-a1101904f99a · outbound

This paper cites Multi-agent actor-critic for mixed cooperative- competitive environments,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Multi-agent actor-critic for mixed cooperative- competitive environments,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.996216Z

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

Observation 9473ac03-f09e-4a34-aff2-58a6d1bd3eec · outbound

This paper cites Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T21:26:24.487206Z

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Observation ef9aecdf-5592-45d8-b10d-93739a96d916 · outbound

This paper cites Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T21:26:24.490884Z

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Observation 676e8d54-0008-4bee-967f-cdbfdfc84c80 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T21:26:24.494990Z

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Observation 1f8e8d99-4f6f-420d-aa24-38c6449b752c · outbound

This paper cites Particle Tracking Data: Bergen DTC Prototype,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Particle Tracking Data: Bergen DTC Prototype,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.984319Z

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Observation 081d50d7-9e31-4fc9-a67e-7e1b03ae8469 · outbound

This paper cites GATE -Geant4 Application for Tomographic Emission: a simulation toolkit for PET and SPECT,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning GATE -Geant4 Application for Tomographic Emission: a simulation toolkit for PET and SPECT,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.971460Z

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.

Observation 2cf17cea-1032-4953-aaf1-4199586de8e1 · outbound

This paper cites GATE V6: A major enhancement of the GATE simula- tion platform enabling modelling of CT and radiotherapy,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning GATE V6: A major enhancement of the GATE simula- tion platform enabling modelling of CT and radiotherapy,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.958701Z

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.

Observation 723aa94a-8bd1-4540-8776-c0a2e1feaf31 · outbound

This paper cites GEANT4 - A simulation toolkit,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning GEANT4 - A simulation toolkit,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.946290Z

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.

Observation 9bd67d7b-91db-4f0f-9e3c-1e90710328ad · outbound

This paper cites Geant4 developments and applications,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Geant4 developments and applications,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.934347Z

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.

Observation d85267aa-523a-4258-9684-be0c1f471700 · outbound

This paper cites Recent developments in GEANT4,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Recent developments in GEANT4,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.923245Z

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.

Observation 63f49150-c2f4-404c-a82c-821470a85285 · outbound

This paper cites Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.903988Z

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.

Observation 255c924f-99f1-433c-9fee-da58613476d0 · outbound

This paper cites The generalisation of student’s problems when several different population variances are involved.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning The generalisation of student’s problems when several different population variances are involved

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.885064Z

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.

Observation 85c32a0d-ec5a-4fe5-8dd9-5e654870d265 · outbound

This paper cites Visualizing the loss landscape of neural nets,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Visualizing the loss landscape of neural nets,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.868807Z

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.

Observation a81a7588-a683-4d73-8366-9709486d644c · outbound

This paper cites Similarity of Neural Network Models: A Survey of Functional and Representational Measures.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Similarity of Neural Network Models: A Survey of Functional and Representational Measures

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-10T21:26:24.534308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38d98c7c-c8e2-43d1-864a-3c950adc544a · outbound

This paper cites On the prediction instability of graph neural net- works,.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning On the prediction instability of graph neural net- works,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.853568Z

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.

Observation d9b290ee-71a5-4d56-8173-5c8a106677fb · outbound

This paper cites His research interests include machine learning and reinforcement learning, with focus on applications in high energy and medical physics.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning His research interests include machine learning and reinforcement learning, with focus on applications in high energy and medical physics

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:26:24.839537Z

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.

Observation e42c9d00-258b-46d3-9593-917f6f9c73b0 · outbound

This paper cites Available: https://doi.org/10.5281/zenodo.7426388.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Available: https://doi.org/10.5281/zenodo.7426388

Reference 2022

Resolution: verified exact; doi, observed 2026-08-10T21:26:24.578223Z

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.

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