# Obtaining Robust Control and Navigation Policies for Multi-Robot Navigation via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2209.03097 · HTML: https://mlsanity.com/paper/2209.03097/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2022.

## Built on by (most cited first, 3 of 3)

- [E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance](https://mlsanity.com/paper/2212.02064/) (2022)
- [Replication of Multi-agent Reinforcement Learning for the "Hide and Seek" Problem](https://mlsanity.com/paper/2310.05430/) (2023)
- [Beyond Specialization: Robust Reinforcement Learning Navigation via Procedural Map Generators](https://mlsanity.com/paper/2605.02528/) (2026)

## Abstract (arXiv)

Multi-robot navigation is a challenging task in which multiple robots must be coordinated simultaneously within dynamic environments. We apply deep reinforcement learning (DRL) to learn a decentralized end-to-end policy which maps raw sensor data to the command velocities of the agent. In order to enable the policy to generalize, the training is performed in different environments and scenarios. The learned policy is tested and evaluated in common multi-robot scenarios like switching a place, an intersection and a bottleneck situation. This policy allows the agent to recover from dead ends and to navigate through complex environments.