Improving the autonomous search and attack capabilities of torpedoes can be achieved by applying deep learning algorithms in the following aspects: 1. **Environmental perception and target detection**: - **Sensor data processing**: Use deep learning to process sensor data such as sonar and radar to improve the perception of the environment. Convolutional neural networks (CNNs) can be used to identify and classify underwater targets. - **Target detection**: Use deep learning models (such as YOLO or Faster R-CNN) to detect and identify targets in real time, thereby improving the torpedo's target recognition ability and accuracy. 2. **Path planning and navigation**: - **Reinforcement learning**: Apply deep reinforcement learning (such as Deep Q-Networks or Proximal Policy Optimization) to optimize path planning, allowing torpedoes to find the best path in complex environments, avoid obstacles and approach targets most effectively. - **Imitation learning**: Through imitation learning, learn the optimal strategy from the operations of humans or expert systems to improve navigation capabilities in various situations. 3. **Multi-target tracking and decision-making**: - **Multi-target tracking**: Track multiple dynamic targets through recurrent neural networks (RNN) or long short-term memory networks (LSTM) to improve the situational awareness and reaction speed of torpedoes. - **Intelligent decision-making system**: Combine deep learning with Bayesian decision theory to help torpedoes make dynamic decisions when facing multiple targets and improve the effectiveness of strikes. 4. **Energy management and endurance optimization**: - **Prediction model**: Use deep learning to predict energy consumption and environmental changes to optimize energy management and improve the endurance and combat capability of torpedoes. - **Adaptive control**: Adaptively adjust the propulsion system through deep neural networks to optimize speed and direction, increase flexibility and accuracy during combat. 5. **Collaboration and communication**: - **Multi-agent collaboration**: Use deep learning to achieve collaborative search and attack between multiple torpedoes, optimize task allocation through distributed learning and communication, and improve combat efficiency and coverage. - **Signal processing and transmission optimization**: Use deep learning to improve signal processing and communication performance to ensure effective communication and information sharing in complex environments. During the implementation process, efficient model training and reasoning, resource optimization, and potential environmental dynamics and uncertainties should be considered. These measures will help significantly improve the autonomous operation capability of torpedoes in complex battlefield environments.