Expert-guided and action-compensated deep reinforcement learning for robust multi-ship collision avoidance in dynamic and uncertain maritime environments

Key Points

• Collision avoidance techniques improve robustness in dynamic maritime settings, enhancing ship safety.
• The improvement is marked, with simulations showing significant advancements in avoidance capabilities.
• Assessment using action-compensated deep reinforcement learning enables high-performing navigation in uncertain conditions.
• This work highlights the need for real-world testing to translate findings into practical maritime applications.