# Deep Reinforcement Learning for Time-Critical Wilderness Search And Rescue Using Drones

arXiv: https://arxiv.org/abs/2405.12800 · HTML: https://mlsanity.com/paper/2405.12800/

## Key facts

- Cited by 5 papers in the map (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 45 references that are in the map.
- First posted on arXiv 21 May 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, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Taming the Noise in Reinforcement Learning via Soft Updates](https://mlsanity.com/paper/1512.08562/) (2015)
- [Localization with Sampling-Argmax](https://mlsanity.com/paper/2110.08825/) (2021)
- [Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2303.03811/) (2023)
- [Catch Planner: Catching High-Speed Targets in the Flight](https://mlsanity.com/paper/2302.04387/) (2023)
- [Enhancing Reinforcement Learning in Sensor Fusion: A Comparative Analysis of Cubature and Sampling-based Integration Methods for Rover Search Planning](https://mlsanity.com/paper/2405.08691/) (2024)
- [Path Planning for Optimal Coverage of Areas with Nonuniform Importance](https://mlsanity.com/paper/2110.09745/) (2021)

## Built on by (most cited first, 5 of 5)

- [Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue](https://mlsanity.com/paper/2411.12967/) (2024)
- [Recurrent Auto-Encoders for Enhanced Deep Reinforcement Learning in Wilderness Search and Rescue Planning](https://mlsanity.com/paper/2502.19356/) (2025)
- [Stone Soup Multi-Target Tracking Feature Extraction For Autonomous Search And Track In Deep Reinforcement Learning Environment](https://mlsanity.com/paper/2503.01293/) (2025)
- [Learning What Matters Now: A Dual-Critic Context-Aware RL Framework for Priority-Driven Information Gain](https://mlsanity.com/paper/2506.06786/) (2025)
- [Interpretable Markov-Based Spatiotemporal Risk Surfaces for Missing-Child Search Planning with Reinforcement Learning and LLM-Based Quality Assurance](https://mlsanity.com/paper/2603.08933/) (2026)

## Abstract (arXiv)

Traditional search and rescue methods in wilderness areas can be time-consuming and have limited coverage. Drones offer a faster and more flexible solution, but optimizing their search paths is crucial. This paper explores the use of deep reinforcement learning to create efficient search missions for drones in wilderness environments. Our approach leverages a priori data about the search area and the missing person in the form of a probability distribution map. This allows the deep reinforcement learning agent to learn optimal flight paths that maximize the probability of finding the missing person quickly. Experimental results show that our method achieves a significant improvement in search times compared to traditional coverage planning and search planning algorithms. In one comparison, deep reinforcement learning is found to outperform other algorithms by over 160%, a difference that can mean life or death in real-world search operations. Additionally, unlike previous work, our approach incorporates a continuous action space enabled by cubature, allowing for more nuanced flight patterns.