# CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving

arXiv: https://arxiv.org/abs/2503.22248 · HTML: https://mlsanity.com/paper/2503.22248/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)
- 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, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [End-to-End Deep Reinforcement Learning for Lane Keeping Assist](https://mlsanity.com/paper/1612.04340/) (2016)
- [Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World](https://mlsanity.com/paper/2009.11212/) (2020)
- [RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE](https://mlsanity.com/paper/2004.01286/) (2020)

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

- [Neuro-symbolic Action Masking for Deep Reinforcement Learning](https://mlsanity.com/paper/2602.10598/) (2026)

## Abstract (arXiv)

Lane keeping in autonomous driving systems requires scenario-specific weight tuning for different objectives. We formulate lane-keeping as a constrained reinforcement learning problem, where weight coefficients are automatically learned along with the policy, eliminating the need for scenario-specific tuning. Empirically, our approach outperforms traditional RL in efficiency and reliability. Additionally, real-world demonstrations validate its practical value for real-world autonomous driving.