mlsanityData to 7 Oct 2026

Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning

arXiv 2402.13481 ↗First posted 21 Feb 2024cs.RO cs.AIcited by 2

On the map: End-to-end autonomous driving → Cooperative and V2X driving

Abstract (arXiv)

Autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the Personality Modeling Network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization compared to traditional MARL methods.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 27 of its 53 references that are in the map. First posted on arXiv 21 Feb 2024.

Builds on

27 of 53 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic11 Nov
  2. 2021Learning to Robustly Negotiate Bi-Directional Lane Usage in High-Conflict Driving Scenarios22 Mar

Other references · 25

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2015Trust Region Policy Optimizationcited by 2,503
  5. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  6. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  7. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  8. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  9. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 475
  10. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 301
  11. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 281
  12. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 281
13 more
  1. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 259
  2. 2018Mean Field Multi-Agent Reinforcement Learningcited by 202
  3. 2019PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settingscited by 178
  4. 2019On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shiftcited by 174
  5. 2019Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learningcited by 53
  6. 2018A Reinforcement Learning Based Approach for Automated Lane Change Maneuverscited by 44
  7. 2019Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Drivingcited by 36
  8. 2020Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationshipscited by 36
  9. 2020Reward Machines for Cooperative Multi-Agent Reinforcement Learningcited by 35
  10. 2016End-to-End Deep Reinforcement Learning for Lane Keeping Assistcited by 27
  11. 2019Reinforcement Learning with Probabilistic Guarantees for Autonomous Drivingcited by 27
  12. 2018Deep reinforcement learning for search, recommendation, and online advertising: a surveycited by 23
  13. 2022Event-Triggered Model Predictive Control with Deep Reinforcement Learning for Autonomous Drivingcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectivescited by 27
  2. 2026Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Drivingcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
  2. 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
  3. 2026Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
  4. 2025Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Driving
  5. 2021Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge
  6. 2024Adaptive Decision-Making for Autonomous Vehicles: A Learning-Enhanced Game-Theoretic Approach in Interactive Environments
  7. 2025Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach
  8. 2020Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning