Permutation-invariant reinforcement learning for waypoint planning in multi-obstacle environments
- PI-based Architecture for Weapon Waypoint Planning: This study focuses on a key challenge in submarine-launched weapons by introducing Permutation-Invariant (PI) reinforcement learning architectures that provide optimal weapon waypoint plans, intercepting moving targets while avoiding a variety of obstacles.
- Overcoming Limitations of Conventional Networks: The PI-based architectures outperform conventional fully connected neural networks (FCNNs) by offering a more adaptable and efficient solution in complex, multi-obstacle environments.
- Practical Impact on Naval Operations: This AI-based framework can assist the Republic of Korea Navy's next-generation submarines by recommending weapon waypoint plans, improving operators' decision-making in urgent, high-risk combat scenarios, and reducing operational risks.
