# An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks

arXiv: https://arxiv.org/abs/2208.12466 · HTML: https://mlsanity.com/paper/2208.12466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1807.09350/) (2018)
- [A Survey on Resource Allocation in Vehicular Networks](https://mlsanity.com/paper/1909.13587/) (2019)
- [Vehicular Communications: A Network Layer Perspective](https://mlsanity.com/paper/1707.09972/) (2017)

## Abstract (arXiv)

This paper presents the extension of the idea of spectrum sharing in the vehicular networks towards the Heterogeneous Vehicular Network(HetVNET) based on multi-agent reinforcement learning. Here, the multiple vehicle-to-vehicle(V2V) links reuse the spectrum of other vehicle-to-interface(V2I) and also those of other networks. The fast-changing environment in vehicular networks limits the idea of centralizing the CSI and allocate the channels. So, the idea of implementing ML-based methods is used here so that it can be implemented in a distributed manner in all vehicles. Here each On-Board Unit(OBU) can sense the signals in the channel and based on that information runs the RL to decide which channel to autonomously take up. Here, each V2V link will be an agent in MARL. The idea is to train the RL model in such a way that these agents will collaborate rather than compete.