# NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem

arXiv: https://arxiv.org/abs/2604.16967 · HTML: https://mlsanity.com/paper/2604.16967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 18 Apr 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 12 of 12)

- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1911.04936/) (2019)
- [Path Planning using Neural A* Search](https://mlsanity.com/paper/2009.07476/) (2020)
- [MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments](https://mlsanity.com/paper/2007.15724/) (2020)
- [TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers](https://mlsanity.com/paper/2212.11730/) (2022)
- [A Reinforcement Learning Approach to the Orienteering Problem with Time Windows](https://mlsanity.com/paper/2011.03647/) (2020)
- [Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling](https://mlsanity.com/paper/2211.06407/) (2022)
- [A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem](https://mlsanity.com/paper/2009.00363/) (2020)
- [A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems](https://mlsanity.com/paper/2305.01883/) (2023)
- [iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning](https://mlsanity.com/paper/2405.00285/) (2024)
- [A Branch-and-Price Algorithm for a Team Orienteering Problem with Fixed-Wing Drones](https://mlsanity.com/paper/1912.04353/) (2019)

## Abstract (arXiv)

Path planning is usually solved by addressing either the (high-level) route planning problem (waypoint sequencing to achieve the final goal) or the (low-level) path planning problem (trajectory prediction between two waypoints avoiding collisions). However, real-world problems usually require simultaneous solutions to the route and path planning subproblems with a holistic and efficient approach. In this paper, we introduce NaviFormer, a deep reinforcement learning model based on a Transformer architecture that solves the global navigation problem by predicting both high-level routes and low-level trajectories. To evaluate NaviFormer, several experiments have been conducted, including comparisons with other algorithms. Results show competitive accuracy from NaviFormer since it can understand the constraints and difficulties of each subproblem and act consequently to improve performance. Moreover, its superior computation speed proves its suitability for real-time missions.