mlsanityData to 7 Oct 2026

OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous Vehicles

arXiv 2410.18112 ↗First posted 9 Oct 2024cs.MA cs.LG cs.ROcited by 0

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

Abstract (arXiv)

Coordination among connected and autonomous vehicles (CAVs) is advancing due to developments in control and communication technologies. However, much of the current work is based on oversimplified and unrealistic task-specific assumptions, which may introduce vulnerabilities. This is critical because CAVs not only interact with their environment but are also integral parts of it. Insufficient exploration can result in policies that carry latent risks, highlighting the need for methods that explore the environment both extensively and efficiently. This work introduces OPTIMA, a novel distributed reinforcement learning framework for cooperative autonomous vehicle tasks. OPTIMA alternates between thorough data sampling from environmental interactions and multi-agent reinforcement learning algorithms to optimize CAV cooperation, emphasizing both safety and efficiency. Our goal is to improve the generality and performance of CAVs in highly complex and crowded scenarios. Furthermore, the industrial-scale distributed training system easily adapts to different algorithms, reward functions, and strategies.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 20 of its 37 references that are in the map. First posted on arXiv 9 Oct 2024.

Builds on

20 of 37 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving19 Nov

Other references · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
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  5. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
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  10. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  11. 2021Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learningcited by 54
  12. 2017RLlib: Abstractions for Distributed Reinforcement Learningcited by 53
7 more
  1. 2021Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimizationcited by 33
  2. 2021Return-based Scaling: Yet Another Normalisation Trick for Deep RLcited by 22
  3. 2019A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillationcited by 12
  4. 2020A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehiclescited by 12
  5. 2024Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challengecited by 8
  6. 2020Safety Challenges for Autonomous Vehicles in the Absence of Connectivitycited by 5
  7. 2023A Diversity Analysis of Safety Metrics Comparing Vehicle Performance in the Lead-Vehicle Interaction Regimecited by 1

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  1. 2023Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem
  2. 2022Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic
  3. 2022Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
  4. 2022Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios
  5. 2022Decentralized cooperative perception for autonomous vehicles: Learning to value the unknown
  6. 2023Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles
  7. 2023Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles
  8. 2022Scalable Model-based Policy Optimization for Decentralized Networked Systems