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Attention-Guided and Role-Aware Reinforcement Learning for Multi-AUV Counter-Game
Summary
This study introduces an advanced multiagent deep reinforcement learning (MADRL) scheme for autonomous underwater vehicles (AUVs) to improve collaborative decision-making in counter-games (CG). The novel approach enhances coordination and adaptability, leading to more efficient and safer missions.
Area of Science:
- Robotics and Control Systems
- Artificial Intelligence
- Marine Engineering
Background:
- Enhancing collaborative decision-making in multi-AUV systems is crucial for complex missions.
- Existing multiagent deep reinforcement learning (MADRL) schemes often lack role awareness and adaptability in dynamic environments like counter-games (CG).
- Autonomous underwater vehicles (AUVs) require sophisticated tactical cognition and context-aware responsiveness for effective operation.
Purpose of the Study:
- To propose an attention-guided, role-aware MADRL scheme for improved AUV collaboration in CG.
- To develop a customized multi-AUV CG model with AUV-specific constraints and a shared reward alignment mechanism.
- To introduce a hybrid decision architecture and a role-driven contrastive learning objective for heterogeneous coordination and diverse policy development.
Main Methods:
- Developed a customized multi-AUV counter-game (CG) model with AUV-specific constraints.
- Implemented a shared reward alignment mechanism to synchronize individual and team objectives.
- Proposed a hybrid decision architecture integrating soft graph attention, recurrent structures, and contrastive role encoding, coupled with a role-driven contrastive learning objective.
Main Results:
- The proposed MADRL scheme demonstrated superiority in adaptability and coordination compared to existing baselines in comparative studies and multiscale games.
- Learned policies exhibited heterogeneous behaviors, including focused fire and decoy tactics, enhancing mission efficiency and safety.
- Model performance analysis and lake trials validated the scheme's applicability and effectiveness.
Conclusions:
- The attention-guided, role-aware MADRL scheme significantly enhances collaborative decision-making and coordination among AUVs in counter-game scenarios.
- The approach fosters heterogeneous coordination and diverse policy development, leading to improved mission outcomes.
- The validated model shows strong potential for real-world applications in marine robotics and autonomous systems.
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