# Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm

arXiv: https://arxiv.org/abs/2012.09134 · HTML: https://mlsanity.com/paper/2012.09134/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding](https://mlsanity.com/paper/2505.19219/) (2025)
- [Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.01270/) (2023)

## Abstract (arXiv)

We develop a new framework for multi-agent collision avoidance problem. The framework combined traditional pathfinding algorithm and reinforcement learning. In our approach, the agents learn whether to be navigated or to take simple actions to avoid their partners via a deep neural network trained by reinforcement learning at each time step. This framework makes it possible for agents to arrive terminal points in abstract new scenarios. In our experiments, we use Unity3D and Tensorflow to build the model and environment for our scenarios. We analyze the results and modify the parameters to approach a well-behaved strategy for our agents. Our strategy could be attached in different environments under different cases, especially when the scale is large.