# End-to-End Deep Reinforcement Learning for Lane Keeping Assist

arXiv: https://arxiv.org/abs/1612.04340 · HTML: https://mlsanity.com/paper/1612.04340/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2016.

## Built on by (most cited first, 27 of 27)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation](https://mlsanity.com/paper/2210.03209/) (2022)
- [OIL: Observational Imitation Learning](https://mlsanity.com/paper/1803.01129/) (2018)
- [Modified DDPG car-following model with a real-world human driving experience with CARLA simulator](https://mlsanity.com/paper/2112.14602/) (2021)
- [Measuring Progress in Deep Reinforcement Learning Sample Efficiency](https://mlsanity.com/paper/2102.04881/) (2021)
- [A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving](https://mlsanity.com/paper/2304.08235/) (2023)
- [Randomized Adversarial Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/1905.05637/) (2019)
- [End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2104.13617/) (2021)
- [Tackling Real-World Autonomous Driving using Deep Reinforcement Learning](https://mlsanity.com/paper/2207.02162/) (2022)
- [Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions](https://mlsanity.com/paper/2402.04168/) (2024)
- [Deep Reinforcement Learning for Advanced Longitudinal Control and Collision Avoidance in High-Risk Driving Scenarios](https://mlsanity.com/paper/2404.19087/) (2024)
- [Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2306.11465/) (2023)
- [Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning](https://mlsanity.com/paper/2204.04403/) (2022)
- [Knowledge-Guided Exploration in Deep Reinforcement Learning](https://mlsanity.com/paper/2210.15670/) (2022)
- [Vision-based DRL Autonomous Driving Agent with Sim2Real Transfer](https://mlsanity.com/paper/2305.11589/) (2023)
- [Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers](https://mlsanity.com/paper/2306.11466/) (2023)
- [Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.13481/) (2024)
- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)
- [Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles](https://mlsanity.com/paper/2112.14710/) (2021)
- [Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning](https://mlsanity.com/paper/2405.19414/) (2024)
- [CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/2503.00783/) (2025)
- [CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving](https://mlsanity.com/paper/2503.22248/) (2025)
- [Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving](https://mlsanity.com/paper/2602.23259/) (2026)
- [Integrating Imitation Learning with Human Driving Data into Reinforcement Learning to Improve Training Efficiency for Autonomous Driving](https://mlsanity.com/paper/2111.11673/) (2021)
- [Reinforced Imitative Trajectory Planning for Urban Automated Driving](https://mlsanity.com/paper/2410.15607/) (2024)
- [Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving](https://mlsanity.com/paper/2504.18931/) (2025)
- [MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning](https://mlsanity.com/paper/2605.10177/) (2026)

## Abstract (arXiv)

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of the environment. Motivated by Google DeepMind's successful demonstrations of learning for games from Breakout to Go, we will propose different methods for autonomous driving using deep reinforcement learning. This is of particular interest as it is difficult to pose autonomous driving as a supervised learning problem as it has a strong interaction with the environment including other vehicles, pedestrians and roadworks. As this is a relatively new area of research for autonomous driving, we will formulate two main categories of algorithms: 1) Discrete actions category, and 2) Continuous actions category. For the discrete actions category, we will deal with Deep Q-Network Algorithm (DQN) while for the continuous actions category, we will deal with Deep Deterministic Actor Critic Algorithm (DDAC). In addition to that, We will also discover the performance of these two categories on an open source car simulator for Racing called (TORCS) which stands for The Open Racing car Simulator. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction with other vehicles. Finally, we explain the effect of some restricted conditions, put on the car during the learning phase, on the convergence time for finishing its learning phase.