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An expert-guided multi-agent reinforcement learning framework with balanced exploration for uncontrolled

Yangyang Duan1, Changming Li1, Bin Guo1

  • 1College of Electrical Engineering, Sichuan University, Chengdu, 610065 China.

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|April 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new multi-agent reinforcement learning (MARL) framework for safer autonomous driving at uncontrolled intersections. The novel approach enhances training efficiency and decision-making performance in complex traffic scenarios.

Keywords:
Attention mechanismAutonomous drivingExpert-guided learningIncentive regularizationMulti-agent reinforcement learning

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Area of Science:

  • Autonomous Driving
  • Artificial Intelligence
  • Reinforcement Learning

Background:

  • Uncontrolled intersections present significant safety and efficiency challenges for autonomous vehicles.
  • Existing multi-agent reinforcement learning (MARL) methods suffer from low training efficiency and suboptimal strategy convergence.

Purpose of the Study:

  • To propose a novel value-based MARL framework to address limitations in autonomous driving at uncontrolled intersections.
  • To enhance training efficiency, safety, and decision-making performance in complex multi-vehicle interactions.

Main Methods:

  • Developed a dynamic value-learning objective function integrating human decision-making elements.
  • Incorporated a nonlinear incentive regularization term to promote optimal joint actions.
  • Implemented an ego external attention mechanism to improve vehicle perception and interaction stability.

Main Results:

  • The proposed algorithm significantly improved decision-making in complex multi-vehicle interactions.
  • Achieved 211% higher training efficiency compared to the VDN baseline in a high-conflict scenario.
  • Demonstrated superior safety rates over game-theoretic methods and higher asymptotic rewards than all baselines.

Conclusions:

  • The novel MARL framework effectively balances safety and efficiency for autonomous driving at uncontrolled intersections.
  • The proposed innovations lead to improved training efficiency, enhanced safety, and superior performance in complex traffic environments.