# Guiding drones by information gain

arXiv: https://arxiv.org/abs/2401.03947 · HTML: https://mlsanity.com/paper/2401.03947/

## 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 1 of its 18 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## 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, 1 of 1)

- [Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark](https://mlsanity.com/paper/2302.00706/) (2023)

## Abstract (arXiv)

The accurate estimation of locations and emission rates of gas sources is crucial across various domains, including environmental monitoring and greenhouse gas emission analysis. This study investigates two drone sampling strategies for inferring source term parameters of gas plumes from atmospheric measurements. Both strategies are guided by the goal of maximizing information gain attained from observations at sequential locations. Our research compares the myopic approach of infotaxis to a far-sighted navigation strategy trained through deep reinforcement learning. We demonstrate the superior performance of deep reinforcement learning over infotaxis in environments with non-isotropic gas plumes.