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

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.

Authors

• Yimian Ding
• Jingzehua Xu
• Guanwen Xie
• Shuai Zhang 0015
• Yi Li

Keywords

• Training
• Autonomous underwater vehicles
• Large language models
• Decision making
• Reinforcement learning
• Robustness
• Real-time systems
• Iterative methods
• Optimization
• Intelligent robots
• Reinforcement Learning Framework
• Underwater Tasks
• State Space
• Flow Field
• Environmental Adaptation
• Situational Awareness
• Iterative Refinement
• Undersea
• Decision-making Capabilities
• Energy Consumption
• Data Rate
• Structural Design
• Task Completion
• Fluid Dynamics
• Adaptive Control
• Surface Current
• Reduce Energy Consumption
• Characteristic Zero
• Navier Stokes Equations
• Adaptive Optimization
• Reinforcement Learning Algorithm
• Markov Decision Process
• Reward Function
• Sea Conditions
• Policy Network
• Deep Q-network
• 3rd Generation
• Geometric Configuration
• Reinforcement Learning Approach