# Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning

arXiv: https://arxiv.org/abs/2402.04894 · HTML: https://mlsanity.com/paper/2402.04894/

## Key facts

- Cited by 7 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 18 of its 45 references that are in the map.
- First posted on arXiv 7 Feb 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, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [An informative path planning framework for UAV-based terrain monitoring](https://mlsanity.com/paper/1809.03870/) (2018)
- [Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search](https://mlsanity.com/paper/1902.10182/) (2019)
- [ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration](https://mlsanity.com/paper/2301.11575/) (2023)
- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [Multiresolution Mapping and Informative Path Planning for UAV-based Terrain Monitoring](https://mlsanity.com/paper/1703.02854/) (2017)
- [Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2203.01642/) (2022)
- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [Informative Path Planning for Mobile Sensing with Reinforcement Learning](https://mlsanity.com/paper/2002.07890/) (2020)
- [Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2209.07660/) (2022)
- [Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targets](https://mlsanity.com/paper/2303.06350/) (2023)
- [Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection](https://mlsanity.com/paper/1908.02901/) (2019)
- [Online Informative Path Planning for Active Information Gathering of a 3D Surface](https://mlsanity.com/paper/2103.09556/) (2021)
- [Monitoring and mapping of crop fields with UAV swarms based on information gain](https://mlsanity.com/paper/2203.11766/) (2022)
- [TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning](https://mlsanity.com/paper/2203.12830/) (2022)
- [Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning](https://mlsanity.com/paper/2108.06618/) (2021)
- [Adaptive Coverage Path Planning for Efficient Exploration of Unknown Environments](https://mlsanity.com/paper/2302.03164/) (2023)

## Built on by (most cited first, 7 of 7)

- [Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning](https://mlsanity.com/paper/2412.02316/) (2024)
- [OffRIPP: Offline RL-based Informative Path Planning](https://mlsanity.com/paper/2409.16830/) (2024)
- [Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning](https://mlsanity.com/paper/2409.16967/) (2024)
- [Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms](https://mlsanity.com/paper/2503.02913/) (2025)
- [DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning](https://mlsanity.com/paper/2410.17186/) (2024)
- [Attention-based Learning for 3D Informative Path Planning](https://mlsanity.com/paper/2506.08434/) (2025)
- [Trajectory Optimization in Single and Dual-UAV Bearing-Only Target Localization](https://mlsanity.com/paper/2606.09188/) (2026)

## Abstract (arXiv)

Autonomous robots are often employed for data collection due to their efficiency and low labour costs. A key task in robotic data acquisition is planning paths through an initially unknown environment to collect observations given platform-specific resource constraints, such as limited battery life. Adaptive online path planning in 3D environments is challenging due to the large set of valid actions and the presence of unknown occlusions. To address these issues, we propose a novel deep reinforcement learning approach for adaptively replanning robot paths to map targets of interest in unknown 3D environments. A key aspect of our approach is a dynamically constructed graph that restricts planning actions local to the robot, allowing us to react to newly discovered static obstacles and targets of interest. For replanning, we propose a new reward function that balances between exploring the unknown environment and exploiting online-discovered targets of interest. Our experiments show that our method enables more efficient target discovery compared to state-of-the-art learning and non-learning baselines. We also showcase our approach for orchard monitoring using an unmanned aerial vehicle in a photorealistic simulator. We open-source our code and model at: https://github.com/dmar-bonn/ipp-rl-3d.