# Deep Reinforcement Learning for Autonomous Driving

arXiv: https://arxiv.org/abs/1811.11329 · HTML: https://mlsanity.com/paper/1811.11329/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 30 of 36)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient](https://mlsanity.com/paper/2406.00681/) (2024)
- [Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model](https://mlsanity.com/paper/2210.04017/) (2022)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety](https://mlsanity.com/paper/2105.10682/) (2021)
- [Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns](https://mlsanity.com/paper/2109.07723/) (2021)
- [A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles](https://mlsanity.com/paper/2105.14218/) (2021)
- [Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies](https://mlsanity.com/paper/2112.11937/) (2021)
- [Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving](https://mlsanity.com/paper/2103.13861/) (2021)
- [Objective-aware Traffic Simulation via Inverse Reinforcement Learning](https://mlsanity.com/paper/2105.09560/) (2021)
- [HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents](https://mlsanity.com/paper/2503.08241/) (2025)
- [Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models](https://mlsanity.com/paper/2304.03290/) (2023)
- [A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2309.01194/) (2023)
- [Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning](https://mlsanity.com/paper/2203.10949/) (2022)
- [CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer](https://mlsanity.com/paper/2206.08883/) (2022)
- [Goal-constrained Sparse Reinforcement Learning for End-to-End Driving](https://mlsanity.com/paper/2103.09189/) (2021)
- [ARLO: A Framework for Automated Reinforcement Learning](https://mlsanity.com/paper/2205.10416/) (2022)
- [GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving](https://mlsanity.com/paper/2206.01488/) (2022)
- [A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges](https://mlsanity.com/paper/2211.06665/) (2022)
- [Safe Inverse Reinforcement Learning via Control Barrier Function](https://mlsanity.com/paper/2212.02753/) (2022)
- [Generative AI for Autonomous Driving: A Review](https://mlsanity.com/paper/2505.15863/) (2025)
- [Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment](https://mlsanity.com/paper/2112.11947/) (2021)
- [Learning Interpretable Policies in Hindsight-Observable POMDPs through Partially Supervised Reinforcement Learning](https://mlsanity.com/paper/2402.09290/) (2024)
- [Controlling an Inverted Pendulum with Policy Gradient Methods-A Tutorial](https://mlsanity.com/paper/2105.07998/) (2021)
- [Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management](https://mlsanity.com/paper/2306.10950/) (2023)
- [Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control](https://mlsanity.com/paper/2502.21057/) (2025)
- [BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2606.08684/) (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)
- [Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning](https://mlsanity.com/paper/2211.15920/) (2022)
- [Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management](https://mlsanity.com/paper/2405.05449/) (2024)

## Abstract (arXiv)

Reinforcement learning has steadily improved and outperform human in lots of traditional games since the resurgence of deep neural network. However, these success is not easy to be copied to autonomous driving because the state spaces in real world are extreme complex and action spaces are continuous and fine control is required. Moreover, the autonomous driving vehicles must also keep functional safety under the complex environments. To deal with these challenges, we first adopt the deep deterministic policy gradient (DDPG) algorithm, which has the capacity to handle complex state and action spaces in continuous domain. We then choose The Open Racing Car Simulator (TORCS) as our environment to avoid physical damage. Meanwhile, we select a set of appropriate sensor information from TORCS and design our own rewarder. In order to fit DDPG algorithm to TORCS, we design our network architecture for both actor and critic inside DDPG paradigm. To demonstrate the effectiveness of our model, We evaluate on different modes in TORCS and show both quantitative and qualitative results.