# Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving

arXiv: https://arxiv.org/abs/2304.08280 · HTML: https://mlsanity.com/paper/2304.08280/

## Key facts

- Cited by 4 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 16 of its 21 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Placed in

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

## Builds on (most cited first, 16 of 16)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety](https://mlsanity.com/paper/1903.00640/) (2019)
- [Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments](https://mlsanity.com/paper/2006.12576/) (2020)
- [Dynamic Input for Deep Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/1907.10994/) (2019)
- [Cooperative Behavior Planning for Automated Driving using Graph Neural Networks](https://mlsanity.com/paper/2202.11376/) (2022)
- [Autonomous and Semi-Autonomous Intersection Management: A Survey](https://mlsanity.com/paper/2006.13133/) (2020)
- [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)
- [An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management](https://mlsanity.com/paper/2207.08655/) (2022)
- [Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2301.12717/) (2023)

## Built on by (most cited first, 4 of 4)

- [FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Driving](https://mlsanity.com/paper/2402.01443/) (2024)
- [A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving](https://mlsanity.com/paper/2402.01465/) (2024)
- [A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach](https://mlsanity.com/paper/2512.11944/) (2025)
- [MIND-Stack: Modular, Interpretable, End-to-End Differentiability for Autonomous Navigation](https://mlsanity.com/paper/2505.21734/) (2025)

## Abstract (arXiv)

Reinforcement learning has received high research interest for developing planning approaches in automated driving. Most prior works consider the end-to-end planning task that yields direct control commands and rarely deploy their algorithm to real vehicles. In this work, we propose a method to employ a trained deep reinforcement learning policy for dedicated high-level behavior planning. By populating an abstract objective interface, established motion planning algorithms can be leveraged, which derive smooth and drivable trajectories. Given the current environment model, we propose to use a built-in simulator to predict the traffic scene for a given horizon into the future. The behavior of automated vehicles in mixed traffic is determined by querying the learned policy. To the best of our knowledge, this work is the first to apply deep reinforcement learning in this manner, and as such lacks a state-of-the-art benchmark. Thus, we validate the proposed approach by comparing an idealistic single-shot plan with cyclic replanning through the learned policy. Experiments with a real testing vehicle on proving grounds demonstrate the potential of our approach to shrink the simulation to real world gap of deep reinforcement learning based planning approaches. Additional simulative analyses reveal that more complex multi-agent maneuvers can be managed by employing the cycling replanning approach.