# An Agile Adaptation Method for Multi-mode Vehicle Communication Networks

arXiv: https://arxiv.org/abs/2408.01429 · HTML: https://mlsanity.com/paper/2408.01429/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 1 of 1)

- [The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research](https://mlsanity.com/paper/2301.07325/) (2023)

## Abstract (arXiv)

This paper focuses on discovering the impact of communication mode allocation on communication efficiency in the vehicle communication networks. To be specific, Markov decision process and reinforcement learning are applied to establish an agile adaptation mechanism for multi-mode communication devices according to the driving scenarios and business requirements. Then, Q-learning is used to train the agile adaptation reinforcement learning model and output the trained model. By learning the best actions to take in different states to maximize the cumulative reward, and avoiding the problem of poor adaptation effect caused by inaccurate delay measurement in unstable communication scenarios. The experiments show that the proposed scheme can quickly adapt to dynamic vehicle networking environment, while achieving high concurrency and communication efficiency.