# Decentralized Cooperative Lane Changing at Freeway Weaving Areas Using Multi-Agent Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2110.08124 · HTML: https://mlsanity.com/paper/2110.08124/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [RLlib: Abstractions for Distributed Reinforcement Learning](https://mlsanity.com/paper/1712.09381/) (2017)
- [Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving](https://mlsanity.com/paper/1905.02680/) (2019)
- [Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.10056/) (2018)
- [Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints](https://mlsanity.com/paper/1904.00231/) (2019)
- [Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment](https://mlsanity.com/paper/1804.06264/) (2018)
- [Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks](https://mlsanity.com/paper/1612.03653/) (2016)
- [A DRL-based Multiagent Cooperative Control Framework for CAV Networks: a Graphic Convolution Q Network](https://mlsanity.com/paper/2010.05437/) (2020)
- [Cooperative Highway Work Zone Merge Control based on Reinforcement Learning in A Connected and Automated Environment](https://mlsanity.com/paper/2001.08581/) (2020)

## Built on by (most cited first, 2 of 2)

- [Safe and Efficient CAV Lane Changing using Decentralised Safety Shields](https://mlsanity.com/paper/2505.01453/) (2025)
- [A Collaborative Safety Shield for Safe and Efficient CAV Lane Changes in Congested On-Ramp Merging](https://mlsanity.com/paper/2602.10007/) (2026)

## Abstract (arXiv)

Frequent lane changes during congestion at freeway bottlenecks such as merge and weaving areas further reduce roadway capacity. The emergence of deep reinforcement learning (RL) and connected and automated vehicle technology provides a possible solution to improve mobility and energy efficiency at freeway bottlenecks through cooperative lane changing. Deep RL is a collection of machine-learning methods that enables an agent to improve its performance by learning from the environment. In this study, a decentralized cooperative lane-changing controller was developed using proximal policy optimization by adopting a multi-agent deep RL paradigm. In the decentralized control strategy, policy learning and action reward are evaluated locally, with each agent (vehicle) getting access to global state information. Multi-agent deep RL requires lower computational resources and is more scalable than single-agent deep RL, making it a powerful tool for time-sensitive applications such as cooperative lane changing. The results of this study show that cooperative lane changing enabled by multi-agent deep RL yields superior performance to human drivers in term of traffic throughput, vehicle speed, number of stops per vehicle, vehicle fuel efficiency, and emissions. The trained RL policy is transferable and can be generalized to uncongested, moderately congested, and extremely congested traffic conditions.