ARXIV:2603.24931 · MULTI-AGENT REINFORCEMENT LEARNING FOR SELF-DRIVING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems

Yifeng Zhang · Jieming Chen · Tingguang Zhou · Tanishq Duhan · Jianghong Dong · Yuhong Cao · +1 at arXiv

A novel MARL framework for self-driving systems that significantly improves safety and efficiency in dense urban traffic through collaborative interaction-aware learning.

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Opportunity summary

Pain A novel MARL framework for self-driving systems that significantly improves safety and efficiency in dense urban traffic through collaborative interaction-aware learning.

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PROBLEM

A novel MARL framework for self-driving systems that significantly improves safety and efficiency in dense urban traffic through collaborative interaction-aware learning. Multi-Agent Reinforcement Learning (MARL) has emerged as a promising approach for developing advanced…

METHOD

Full abstract

Multi-Agent Self-Driving (MASD) systems provide an effective solution for coordinating autonomous vehicles to reduce congestion and enhance both safety and operational efficiency in future intelligent transportation systems. Multi-Agent Reinforcement Learning (MARL) has emerged as a promising approach for developing advanced end-to-end MASD systems. However, achieving efficient and safe collaboration in dynamic MASD systems remains a significant challenge in dense scenarios with complex agent interactions. To address this challenge, we propose a novel collaborative(CO-) interaction-aware(-IN) MARL framework, named COIN. Specifically, we develop a new counterfactual individual-global twin delayed deep deterministic policy gradient (CIG-TD3) algorithm, crafted in a "centralized training, decentralized execution" (CTDE) manner, which aims to jointly optimize the individual objectives (navigation) and the global objectives (collaboration) of agents. We further introduce a dual-level interaction-aware centralized critic architecture that captures both local pairwise interactions and global system-level dependencies, enabling more accurate global value estimation and improved credit assignment for collaborative policy learning. We conduct extensive simulation experiments in dense urban traffic environments, which demonstrate that COIN consistently outperforms other advanced baseline methods in both safety and efficiency across various system sizes. These results highlight its superiority in complex and dynamic MASD scenarios, as further validated through real-world robot demonstrations. Supplementary videos are available at https://marmotlab.github.io/COIN/

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. We conduct extensive simulation experiments in dense urban traffic environments, which demonstrate that COIN consistently outperforms other advanced baseline methods in both safety and…

WHY NOW

Multi-Agent Reinforcement Learning for Self-Driving moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.

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Opportunity summary

Score8.0

PainA novel MARL framework for self-driving systems that significantly improves safety and efficiency in dense urban traffic through collaborative interaction-aware learning.

Evidence0 refs | 0 sources | 17% coverage

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Analysis summary

A novel MARL framework for self-driving systems that significantly improves safety and efficiency in dense urban traffic through collaborative interaction-aware learning.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

A novel MARL framework for self-driving systems that significantly improves safety and efficiency in dense urban traffic through collaborative interaction-aware learning.

Segment

Multi-Agent Reinforcement Learning for Self-Driving

Adoption evidence

No public code link in the paper record yet

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8.0/10 public viability

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Competitive landscape

A novel MARL framework for self-driving systems that significantly improves safety and efficiency in dense urban traffic through collaborative interaction-aware learning.

Segment

Multi-Agent Reinforcement Learning for Self-Driving

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems

COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems

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