# Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments

arXiv: https://arxiv.org/abs/2006.12576 · HTML: https://mlsanity.com/paper/2006.12576/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 13 of 13)

- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Cooperative Behavior Planning for Automated Driving using Graph Neural Networks](https://mlsanity.com/paper/2202.11376/) (2022)
- [A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.12073/) (2021)
- [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)
- [Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric](https://mlsanity.com/paper/2302.01259/) (2023)
- [Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving](https://mlsanity.com/paper/2304.08280/) (2023)
- [Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars](https://mlsanity.com/paper/2102.00675/) (2021)
- [Subequivariant Reinforcement Learning Framework for Coordinated Motion Control](https://mlsanity.com/paper/2403.15100/) (2024)
- [Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments](https://mlsanity.com/paper/2201.12776/) (2022)
- [Deep Occupancy-Predictive Representations for Autonomous Driving](https://mlsanity.com/paper/2303.04218/) (2023)
- [Predictive Traffic Rule Compliance using Reinforcement Learning](https://mlsanity.com/paper/2503.22925/) (2025)
- [Learning Isometric Embeddings of Road Networks using Multidimensional Scaling](https://mlsanity.com/paper/2504.17534/) (2025)

## Abstract (arXiv)

Most reinforcement learning approaches used in behavior generation utilize vectorial information as input. However, this requires the network to have a pre-defined input-size -- in semantic environments this means assuming the maximum number of vehicles. Additionally, this vectorial representation is not invariant to the order and number of vehicles. To mitigate the above-stated disadvantages, we propose combining graph neural networks with actor-critic reinforcement learning. As graph neural networks apply the same network to every vehicle and aggregate incoming edge information, they are invariant to the number and order of vehicles. This makes them ideal candidates to be used as networks in semantic environments -- environments consisting of objects lists. Graph neural networks exhibit some other advantages that make them favorable to be used in semantic environments. The relational information is explicitly given and does not have to be inferred. Moreover, graph neural networks propagate information through the network and can gather higher-degree information. We demonstrate our approach using a highway lane-change scenario and compare the performance of graph neural networks to conventional ones. We show that graph neural networks are capable of handling scenarios with a varying number and order of vehicles during training and application.