Deep Reinforcement Learning for Ship Dynamic Positioning: A Live Explainable AI Approach on an Autonomous Ferry Prototype

Deep reinforcement learning (DRL) has the potential to provide an operationally efficient and rapidly deployable alternative to classical ship dynamic-positioning (DP) controllers; however, the limited transparency of neural network-based policies currently hinders their adoption on regulated vessels. This paper investigates whether a Proximal Policy Optimization (PPO) controller, augmented with live Shapley Additive Explanations (SHAP), can be trained, evaluated, and deployed on a physical autonomous vessel to perform the DP task while keeping human operators “in the loop”. To achieve this, we developed a ROS-based Gymnasium environment around a 3-DOF ship simulator with a four-thruster action space, which decomposes azimuth forces into surge and sway components to preserve SHAP additivity. The actor-critic network, consisting of two 64-unit tanh layers, was trained for 500,000 time steps (approximately 35 h) using a shaped reward that balances pose error, velocity, and thruster wear. Default Stable-Baselines3 hyperparameters were retained to ensure robustness. Sea trials in Trondheim, Norway confirmed that the proposed controller: (i) held station inside the mandated error of 0.25 m and 5 degrees, with an action space [-10,10] m (ii) followed straight-line and spline paths with metre-level accuracy, due to not integrator and constantly updating target, and (iii) matched simulated behavior except for mild oscillations attributed to velocity calculations and disturbances. During experimental runs, a four-window dashboard streamed SHAP bar plots and force arrows at 4 Hz. Operators consistently observed that the dominant feature, such as pose error or sway velocity, correctly drove the corresponding thruster pair, providing validation of the explanation pipeline. To the author’s knowledge, this is the first end-to-end demonstration of DRL combined with live explainable AI (XAI) on a real marine craft.