# Multi-Agent Path Planning Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2110.01460 · HTML: https://mlsanity.com/paper/2110.01460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2002.05513/) (2020)
- [Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching](https://mlsanity.com/paper/1910.02591/) (2019)
- [Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows](https://mlsanity.com/paper/2010.02068/) (2020)

## Built on by (most cited first, 1 of 1)

- [Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.01270/) (2023)

## Abstract (arXiv)

In this paper a deep reinforcement based multi-agent path planning approach is introduced. The experiments are realized in a simulation environment and in this environment different multi-agent path planning problems are produced. The produced problems are actually similar to a vehicle routing problem and they are solved using multi-agent deep reinforcement learning. In the simulation environment, the model is trained on different consecutive problems in this way and, as the time passes, it is observed that the model's performance to solve a problem increases. Always the same simulation environment is used and only the location of target points for the agents to visit is changed. This contributes the model to learn its environment and the right attitude against a problem as the episodes pass. At the end, a model who has already learned a lot to solve a path planning or routing problem in this environment is obtained and this model can already find a nice and instant solution to a given unseen problem even without any training. In routing problems, standard mathematical modeling or heuristics seem to suffer from high computational time to find the solution and it is also difficult and critical to find an instant solution. In this paper a new solution method against these points is proposed and its efficiency is proven experimentally.