A Study on Multi-agent Reinforcement Learning for Autonomous Distribution Vehicles

Author:

Ergün Serap1

Affiliation:

1. Isparta University of Applied Sciences

Abstract

Abstract A self-driving car, also called an autonomous vehicle, is capable of sensing the environment and driving without any human intervention, utilizing software algorithms and a fully automated driving system. This allows the vehicle to manage external conditions similar to a human. Safe and efficient delivery services require road controls and road restrictions to prevent accidents and reduce damage from unexpected technical failures. This study formulates the Autonomous Delivery Vehicles optimization problem and proposes a multi-agent reinforcement learning method using the analytically calculated shortest path information.

Publisher

Research Square Platform LLC

Reference16 articles.

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2. Filos, A., Tigkas, P., McAllister, R., Rhinehart, N., Levine, S., & Gal, Y., 2020. Can autonomous vehicles identify, recover from, and adapt to distribution shifts?. In International Conference on Machine Learning (pp. 3145–3153). PMLR.

3. Fruit, R., 2019. Exploration-exploitation dilemma in Reinforcement Learning under various form of prior knowledge (Doctoral dissertation, Université de Lille 1, Sciences et Technologies; CRIStAL UMR 9189).

4. Multi-agent deep reinforcement learning: a survey;Gronauer S;Artificial Intelligence Review,2022

5. Multimodal transport distribution model for autonomous driving vehicles based on improved ALNS;Guo Y;Alexandria Engineering Journal,2022

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