# Performance Comparison of Deep RL Algorithms for Mixed Traffic Cooperative Lane-Changing

arXiv: https://arxiv.org/abs/2407.02521 · HTML: https://mlsanity.com/paper/2407.02521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 4 of 4)

- [End-to-End Race Driving with Deep Reinforcement Learning](https://mlsanity.com/paper/1807.02371/) (2018)
- [Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning](https://mlsanity.com/paper/1904.10171/) (2019)
- [Continuous Control for Automated Lane Change Behavior Based on Deep Deterministic Policy Gradient Algorithm](https://mlsanity.com/paper/1906.02275/) (2019)
- [Identification of Driving Heterogeneity using Action-chains](https://mlsanity.com/paper/2307.16843/) (2023)

## Abstract (arXiv)

Lane-changing (LC) is a challenging scenario for connected and automated vehicles (CAVs) because of the complex dynamics and high uncertainty of the traffic environment. This challenge can be handled by deep reinforcement learning (DRL) approaches, leveraging their data-driven and model-free nature. Our previous work proposed a cooperative lane-changing in mixed traffic (CLCMT) mechanism based on TD3 to facilitate an optimal lane-changing strategy. This study enhances the current CLCMT mechanism by considering both the uncertainty of the human-driven vehicles (HVs) and the microscopic interactions between HVs and CAVs. The state-of-the-art (SOTA) DRL algorithms including DDPG, TD3, SAC, and PPO are utilized to deal with the formulated MDP with continuous actions. Performance comparison among the four DRL algorithms demonstrates that DDPG, TD3, and PPO algorithms can deal with uncertainty in traffic environments and learn well-performed LC strategies in terms of safety, efficiency, comfort, and ecology. The PPO algorithm outperforms the other three algorithms, regarding a higher reward, fewer exploration mistakes and crashes, and a more comfortable and ecology LC strategy. The improvements promise CLCMT mechanism greater advantages in the LC motion planning of CAVs.