# Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving

arXiv: https://arxiv.org/abs/2403.11914 · HTML: https://mlsanity.com/paper/2403.11914/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world](https://mlsanity.com/paper/2206.09889/) (2022)
- [Social Attention for Autonomous Decision-Making in Dense Traffic](https://mlsanity.com/paper/1911.12250/) (2019)
- [Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization](https://mlsanity.com/paper/2110.13827/) (2021)
- [A First Look at Ad-block Detection: A New Arms Race on the Web](https://mlsanity.com/paper/1605.05841/) (2016)
- [Dynamic Input for Deep Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/1907.10994/) (2019)
- [Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.04046/) (2020)
- [Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion](https://mlsanity.com/paper/2010.05436/) (2020)
- [Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning](https://mlsanity.com/paper/2106.06369/) (2021)
- [Learning Continuous Control with Geometric Regularity from Robot Intrinsic Symmetry](https://mlsanity.com/paper/2306.16316/) (2023)

## Built on by (most cited first, 1 of 1)

- [Structured Diversity Control: A Dual-Level Framework for Group-Aware Multi-Agent Coordination](https://mlsanity.com/paper/2506.18651/) (2025)

## Abstract (arXiv)

Active traffic management with autonomous vehicles offers the potential for reduced congestion and improved traffic flow. However, developing effective algorithms for real-world scenarios requires overcoming challenges related to infinite-horizon traffic flow and partial observability. To address these issues and further decentralize traffic management, we propose an asymmetric actor-critic model that learns decentralized cooperative driving policies for autonomous vehicles using single-agent reinforcement learning. By employing attention neural networks with masking, our approach efficiently manages real-world traffic dynamics and partial observability, eliminating the need for predefined agents or agent-specific experience buffers in multi-agent reinforcement learning. Extensive evaluations across various traffic scenarios demonstrate our method's significant potential in improving traffic flow at critical bottleneck points. Moreover, we address the challenges posed by conservative autonomous vehicle driving behaviors that adhere strictly to traffic rules, showing that our cooperative policy effectively alleviates potential slowdowns without compromising safety.