Paper Citation Record · LEDGER

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2603.06607.

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

pith.paper-citation-record.v1
2603.06607 v2

Coverage vector

measured 41 of 41 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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Outbound references

Observation 9f1c05bb-9e6e-47b4-9ede-71541b8b4d0d · outbound

This paper cites Multi-agent drl for resource allocation in vehicular networks: A comparative study,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Multi-agent drl for resource allocation in vehicular networks: A comparative study,

Reference 1

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Observation 374ba54c-e49a-4153-9a02-3b7532b17dcb · outbound

This paper cites Recent advances and future trends in vehicular communication systems: A comprehensive survey,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Recent advances and future trends in vehicular communication systems: A comprehensive survey,

Reference 2

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Observation dd203d4d-334f-4b85-8187-2b9b1989a1cb · outbound

This paper cites Multiuser resource control with deep reinforcement learning in iot edge comput- ing,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Multiuser resource control with deep reinforcement learning in iot edge comput- ing,

Reference 3

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Observation 4cc0e15b-acfc-4ae4-af43-c86f058257f7 · outbound

This paper cites Deep reinforcement learning based resource allocation for v2v communications,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Deep reinforcement learning based resource allocation for v2v communications,

Reference 4

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This paper cites an unresolved cited work.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Unresolved cited work

Reference 5

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Observation 511f70b3-1e08-43aa-a109-56bc96060968 · outbound

This paper cites Spectrum sharing in vehicular networks based on multi-agent reinforcement learning,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Spectrum sharing in vehicular networks based on multi-agent reinforcement learning,

Reference 6

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Observation fbb8b2fa-a04a-416b-a21e-4a6446d2c666 · outbound

This paper cites Multi-agent RL enables decentralized spectrum access in vehicular networks,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Multi-agent RL enables decentralized spectrum access in vehicular networks,

Reference 7

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Observation 3cb9e088-f01e-43b0-bf8b-e8502403d006 · outbound

This paper cites Multi-agent reinforcement learning-based decentralized spectrum access in vehic- ular networks with emergent communication,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Multi-agent reinforcement learning-based decentralized spectrum access in vehic- ular networks with emergent communication,

Reference 8

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Observation 982bb0da-cf4c-480c-9155-70b5c1cc87b4 · outbound

This paper cites Resource allocation in V2X communications based on multi-agent reinforcement learning with attention mechanism,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Resource allocation in V2X communications based on multi-agent reinforcement learning with attention mechanism,

Reference 9

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This paper cites Mean-field aided multi-agent reinforcement learning for resource allocation in vehicular networks,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Mean-field aided multi-agent reinforcement learning for resource allocation in vehicular networks,

Reference 10

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Observation ac01e186-28d3-4e1f-ad11-5c21aa76f15e · outbound

This paper cites Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications

Reference 11

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Observation 731b35d2-d8bf-493f-adc8-fe57b2b25cd8 · outbound

This paper cites Meta-reinforcement learning based resource allocation for dynamic v2x communications,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Meta-reinforcement learning based resource allocation for dynamic v2x communications,

Reference 12

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Observation 10b5bb66-4620-4565-8cc3-9b10e507eb35 · outbound

This paper cites Multitimescale control and communications with deep reinforcement learning—part ii: Control- aware radio resource allocation,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Multitimescale control and communications with deep reinforcement learning—part ii: Control- aware radio resource allocation,

Reference 13

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This paper cites A review of cooperative multi-agent deep reinforcement learning,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking A review of cooperative multi-agent deep reinforcement learning,

Reference 14

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Observation df126020-9847-4c4d-94d5-6dc1312ef6d7 · outbound

This paper cites Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 15

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Observation 3cced00c-6889-4909-a1d6-e9c04a97e1bb · outbound

This paper cites Proximal Policy Optimization Algorithms.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Proximal Policy Optimization Algorithms

Reference 16

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Observation 3320a770-529f-4dbc-b0a6-571027f34d67 · outbound

This paper cites Deep reinforcement learning based resource allocation with heterogeneous qos for cellular v2x,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Deep reinforcement learning based resource allocation with heterogeneous qos for cellular v2x,

Reference 17

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Observation 9a3779d8-374c-4d97-901a-1b71e5e04ff9 · outbound

This paper cites Spectrum-energy-efficient mode selection and resource allocation for heterogeneous v2x networks: A fed- erated multi-agent deep reinforcement learning approach,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Spectrum-energy-efficient mode selection and resource allocation for heterogeneous v2x networks: A fed- erated multi-agent deep reinforcement learning approach,

Reference 18

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Observation b764cb70-958b-469f-bc43-f3fcb156e0ac · outbound

This paper cites A hybrid multi-agent reinforcement learning approach for spectrum sharing in vehicular networks,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking A hybrid multi-agent reinforcement learning approach for spectrum sharing in vehicular networks,

Reference 19

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Observation 4d9c1c0a-e034-42f3-95f8-ced5e59cf4bb · outbound

This paper cites Federated reinforcement learning for resource allocation in v2x networks,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Federated reinforcement learning for resource allocation in v2x networks,

Reference 20

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Observation 49bb79f2-027a-42cc-a08c-18958143a13b · outbound

This paper cites Semantic- aware resource allocation based on deep reinforcement learning for 5g- v2x hetnets,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Semantic- aware resource allocation based on deep reinforcement learning for 5g- v2x hetnets,

Reference 21

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Observation 7360ce8d-3795-4b10-8e6c-16d6fad6459b · outbound

This paper cites Deep reinforcement learning for multi- objective resource allocation in multi-platoon cooperative vehicular networks,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Deep reinforcement learning for multi- objective resource allocation in multi-platoon cooperative vehicular networks,

Reference 22

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Observation 01bacd2b-7915-4c8d-a8e2-11ed06589450 · outbound

This paper cites Enabling adaptive optimization of energy efficiency and quality of service in nr-v2x communications via multiagent deep reinforcement learning,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Enabling adaptive optimization of energy efficiency and quality of service in nr-v2x communications via multiagent deep reinforcement learning,

Reference 23

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This paper cites Aoi-aware resource allocation for platoon-based c-v2x networks via multi-agent multi-task reinforcement learning,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Aoi-aware resource allocation for platoon-based c-v2x networks via multi-agent multi-task reinforcement learning,

Reference 24

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning

Reference 25

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This paper cites Deep reinforcement learning for autonomous internet of things: Model, ap- plications and challenges,.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Deep reinforcement learning for autonomous internet of things: Model, ap- plications and challenges,

Reference 26

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

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Multi-agent actor-critic for mixed cooperative-competitive environ- ments,

Reference 27

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Continuous control with deep reinforcement learning,

Reference 28

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This paper cites Technical Specification Group Radio Access Network; Study LTE- Based V2X Services; (Release 14),.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Technical Specification Group Radio Access Network; Study LTE- Based V2X Services; (Release 14),

Reference 29

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This paper cites TR 103 766, no.

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking TR 103 766, no

Reference 30

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Delay- optimal dynamic mode selection and resource allocation in device-to- device communications—part ii: Practical algorithm,

Reference 31

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Lenient learning in independent-learner stochastic cooperative games,

Reference 32

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Leveraging procedural generation to benchmark reinforcement learning,

Reference 33

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Human-level control through deep reinforcement learning,

Reference 34

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Hysteretic q-learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams,

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Asynchronous methods for deep reinforcement learning,

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Value-decomposition networks for cooperative multi-agent learning,

Reference 37

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Qmix: Monotonic value function factorization for deep multi-agent reinforcement learning,

Reference 38

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking The surprising effectiveness of ppo in cooperative, multi-agent games,

Reference 39

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking Performance analysis of device-to-device communications with dynamic interference using stochastic petri nets,

Reference 40

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Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

Reference 2022

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