TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration | Paper Citation Record

Observation 5ea463de-5d01-40db-9968-7dd5eb182b35 · outbound

This paper cites Habitat: A platform for embodied ai research,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Habitat: A platform for embodied ai research,

Reference 1

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This paper cites A call for embodied AI.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration A call for embodied AI

Reference 2

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This paper cites Artificial intelligence based object detection and traffic prediction by autonomous vehicles–a review,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Artificial intelligence based object detection and traffic prediction by autonomous vehicles–a review,

Reference 3

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This paper cites Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI

Reference 4

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This paper cites Generative diffusion-based contract design for efficient ai twin migration in vehicular embodied ai networks,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Generative diffusion-based contract design for efficient ai twin migration in vehicular embodied ai networks,

Reference 5

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This paper cites Embodied AI-Enhanced Vehicular Networks: An Integrated Large Language Models and Reinforcement Learning Method.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Embodied AI-Enhanced Vehicular Networks: An Integrated Large Language Models and Reinforcement Learning Method

Reference 6

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Observation 1d061731-6eb5-474a-956e-e3de95939415 · outbound

This paper cites Language models meet world models: Embodied experiences enhance language models,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Language models meet world models: Embodied experiences enhance language models,

Reference 7

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Observation aae61e56-8b61-4515-86e0-96f9831057d3 · outbound

This paper cites Intelligent cockpit for intelligent connected vehicles: Definition, taxonomy, technology and evaluation,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Intelligent cockpit for intelligent connected vehicles: Definition, taxonomy, technology and evaluation,

Reference 8

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This paper cites Scenario-function system for automotive intelligent cockpits: Framework, research progress and perspectives,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Scenario-function system for automotive intelligent cockpits: Framework, research progress and perspectives,

Reference 9

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Observation 0771b3a2-6b60-4ee7-9119-77d3c3116151 · outbound

This paper cites Learning-based incentive mechanism for task freshness-aware vehic- ular twin migration,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Learning-based incentive mechanism for task freshness-aware vehic- ular twin migration,

Reference 10

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Observation 1a1c428f-648f-4f4d-a658-fa2c05d9d616 · outbound

This paper cites Multiagent deep reinforcement learning for dynamic avatar migration in aiot-enabled vehicular metaverses with trajectory prediction,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Multiagent deep reinforcement learning for dynamic avatar migration in aiot-enabled vehicular metaverses with trajectory prediction,

Reference 11

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Observation 4130b3ef-8143-46de-a70e-7ef071c7689b · outbound

This paper cites A multi- leader multi-follower game-based analysis for incentive mechanisms in socially-aware mobile crowdsensing,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration A multi- leader multi-follower game-based analysis for incentive mechanisms in socially-aware mobile crowdsensing,

Reference 12

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This paper cites Stackelberg game-based computation offloading in social and cognitive industrial internet of things,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Stackelberg game-based computation offloading in social and cognitive industrial internet of things,

Reference 13

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Observation d58aed86-9e51-47fe-b87c-b1cfa792907e · outbound

This paper cites Multiagent federated reinforcement learning for secure incentive mechanism in intelligent cyber–physical systems,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Multiagent federated reinforcement learning for secure incentive mechanism in intelligent cyber–physical systems,

Reference 14

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This paper cites Tiny machine learning for concept drift,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Tiny machine learning for concept drift,

Reference 15

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This paper cites A compre- hensive survey of few-shot learning: Evolution, applications, challenges, and opportunities,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration A compre- hensive survey of few-shot learning: Evolution, applications, challenges, and opportunities,

Reference 16

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This paper cites Strategically efficient exploration in competitive multi-agent reinforcement learning,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Strategically efficient exploration in competitive multi-agent reinforcement learning,

Reference 17

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Observation 30ab5987-b15e-450e-8d2a-f4bd9f8d80bd · outbound

This paper cites Embodied AI-empowered Low Altitude Economy: Integrated Sensing, Communications, Computation, and Control (ISC3).

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Embodied AI-empowered Low Altitude Economy: Integrated Sensing, Communications, Computation, and Control (ISC3)

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This paper cites Embodied Understanding of Driving Scenarios.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Embodied Understanding of Driving Scenarios

Reference 19

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Observation ffa3db40-bc7d-4423-a99d-0c909441b830 · outbound

This paper cites Multi-attribute auction-based resource allocation for twins migration in vehicular metaverses: A gpt-based drl approach,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Multi-attribute auction-based resource allocation for twins migration in vehicular metaverses: A gpt-based drl approach,

Reference 20

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Observation d7310afd-76c1-4a33-a170-c00504e049c1 · outbound

This paper cites Learning-based incentive mechanism for task freshness-aware vehic- ular twin migration,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Learning-based incentive mechanism for task freshness-aware vehic- ular twin migration,

Reference 21

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Observation 68c1b865-7f94-4f71-b8df-0747525ff2ce · outbound

This paper cites Tiny multiagent drl for twins migration in uav metaverses: A multileader multifollower stackelberg game approach,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Tiny multiagent drl for twins migration in uav metaverses: A multileader multifollower stackelberg game approach,

Reference 22

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This paper cites When metaverses meet vehicle road cooperation: Multiagent drl-based stackelberg game for vehicular twins migration,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration When metaverses meet vehicle road cooperation: Multiagent drl-based stackelberg game for vehicular twins migration,

Reference 23

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This paper cites End-to-end multitarget flexible job shop scheduling with deep reinforcement learning,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration End-to-end multitarget flexible job shop scheduling with deep reinforcement learning,

Reference 24

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This paper cites Strangeness-driven exploration in multi-agent reinforcement learning,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Strangeness-driven exploration in multi-agent reinforcement learning,

Reference 25

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This paper cites Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning

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This paper cites Episodic multi-agent reinforcement learning with curiosity- driven exploration,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Episodic multi-agent reinforcement learning with curiosity- driven exploration,

Reference 27

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This paper cites Self-Motivated Multi-Agent Exploration.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Self-Motivated Multi-Agent Exploration

Reference 28

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This paper cites Online-s2t: A lightweight distributed online reinforcement learning training framework for resource-constrained devices,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Online-s2t: A lightweight distributed online reinforcement learning training framework for resource-constrained devices,

Reference 29

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This paper cites Pops: Policy pruning and shrinking for deep reinforcement learning,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Pops: Policy pruning and shrinking for deep reinforcement learning,

Reference 30

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This paper cites Compressing deep reinforcement learning networks with a dynamic structured prun- ing method for autonomous driving,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Compressing deep reinforcement learning networks with a dynamic structured prun- ing method for autonomous driving,

Reference 31

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This paper cites Embodied artificial intelligence,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Embodied artificial intelligence,

Reference 32

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This paper cites Hybrid reconfigurable intelligent meta- surfaces: Enabling simultaneous tunable reflections and sensing for 6g wireless communications,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Hybrid reconfigurable intelligent meta- surfaces: Enabling simultaneous tunable reflections and sensing for 6g wireless communications,

Reference 33

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This paper cites Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

Reference 34

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Observation 8a460c39-2ca5-41d2-8d81-11e42152f583 · outbound

This paper cites Joint user association and resource pricing for metaverse: Distributed and centralized approaches,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Joint user association and resource pricing for metaverse: Distributed and centralized approaches,

Reference 35

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This paper cites Locally adaptive struc- ture and texture similarity for image quality assessment,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Locally adaptive struc- ture and texture similarity for image quality assessment,

Reference 36

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Observation aa8c4ace-7243-41bd-a682-3fe183918b3a · outbound

This paper cites Attention-based qoe-aware digital twin empowered edge computing for immersive virtual reality,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Attention-based qoe-aware digital twin empowered edge computing for immersive virtual reality,

Reference 37

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Observation 4f335e9e-3621-455d-bf34-df4ea31558f7 · outbound

This paper cites Attention-aware resource allocation and qoe analysis for metaverse xurllc services,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Attention-aware resource allocation and qoe analysis for metaverse xurllc services,

Reference 38

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TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Unresolved cited work

Reference 39

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Observation e8df5c03-1426-4b38-8c43-c7ff544c2e3a · outbound

This paper cites Privacy- preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach,.

TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration Privacy- preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach,

Reference 40

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