# Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization

arXiv: https://arxiv.org/abs/2209.06332 · HTML: https://mlsanity.com/paper/2209.06332/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation](https://mlsanity.com/paper/1703.00420/) (2017)
- [Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight](https://mlsanity.com/paper/1902.03701/) (2019)
- [Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition](https://mlsanity.com/paper/2103.12883/) (2021)
- [Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehicles](https://mlsanity.com/paper/2112.13724/) (2021)
- [Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles](https://mlsanity.com/paper/2206.15211/) (2022)
- [Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path](https://mlsanity.com/paper/2003.01641/) (2020)
- [Using Soft Actor-Critic for Low-Level UAV Control](https://mlsanity.com/paper/2010.02293/) (2020)
- [How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV](https://mlsanity.com/paper/1702.07600/) (2017)
- [Trajectory Planning for Hybrid Unmanned Aerial Underwater Vehicles with Smooth Media Transition](https://mlsanity.com/paper/2112.13819/) (2021)

## Built on by (most cited first, 1 of 1)

- [Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning](https://mlsanity.com/paper/2406.01952/) (2024)

## Abstract (arXiv)

Previous works showed that Deep-RL can be applied to perform mapless navigation, including the medium transition of Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs). This paper presents new approaches based on the state-of-the-art actor-critic algorithms to address the navigation and medium transition problems for a HUAUV. We show that a double critic Deep-RL with Recurrent Neural Networks improves the navigation performance of HUAUVs using solely range data and relative localization. Our Deep-RL approaches achieved better navigation and transitioning capabilities with a solid generalization of learning through distinct simulated scenarios, outperforming previous approaches.