Neural Control (Reinforcement Learning) for Tanker Heading

This system employs neural control (reinforcement learning) to autonomously regulate tanker vessel heading. Through neural networks and reinforcement learning algorithms, the tanker can automatically adjust its course to adapt to varying environmental conditions and mission requirements. Key implementation aspects include: - Deep Q-Networks (DQN) or Policy Gradient methods for learning optimal navigation policies - State space representation incorporating position, heading, wind/current data - Reward functions designed for safety, efficiency and course stability - Neural network architecture with multiple hidden layers for processing sensor inputs This technology enables intelligent navigation and operational capabilities, significantly enhancing safety and efficiency. Neural control represents cutting-edge technology applicable to various autonomous navigation systems and intelligent transportation domains. The system continuously learns from environmental interactions, improving performance through iterative policy updates and experience replay mechanisms.