# Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks

arXiv: https://arxiv.org/abs/2506.15082 · HTML: https://mlsanity.com/paper/2506.15082/

## 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 8 of its 27 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## 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, 8 of 8)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Deep Drone Racing: From Simulation to Reality with Domain Randomization](https://mlsanity.com/paper/1905.09727/) (2019)
- [Learning Vision-Based Bipedal Locomotion for Challenging Terrain](https://mlsanity.com/paper/2309.14594/) (2023)
- [Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehicles](https://mlsanity.com/paper/2410.00120/) (2024)
- [Towards a Sustainable Internet-of-Underwater-Things based on AUVs, SWIPT, and Reinforcement Learning](https://mlsanity.com/paper/2302.10368/) (2023)

## Abstract (arXiv)

This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in underwater environments. Departing from traditional RL architectures, the proposed framework integrates an environment-aware network module that dynamically captures flow field data, effectively embedding this critical environmental information into the state space. This integration facilitates real-time environmental adaptation, significantly enhancing the AUV's situational awareness and decision-making capabilities. Furthermore, the framework incorporates AUV structure characteristics into the optimization process, employing a large language model (LLM)-based iterative refinement mechanism that leverages both environmental conditions and training outcomes to optimize task performance. Comprehensive experimental evaluations demonstrate the framework's superior performance, robustness and adaptability.