Policy-Splitting Proximal Policy Optimization for Small Unmanned Surface Vehicle Navigation: A Simulation Study

This study examines whether an explicit separation of navigation and avoidance yields useful control structure and performance gains for small unmanned surface vehicles in simulation. The policy-splitting proximal policy optimization actor (PSN-PPO) combines a navigation mean, a bounded avoidance residual, and a geometric risk gate with a learnable exponent, while retaining the PPO update and a separate critic. Five independent models per method are trained in each of five scenarios and evaluated on common nominal and perturbed cases. Crossing arrival is 100% for PSN-PPO versus 80% for PPO, but arrival without soft-domain intrusion is 25.6% versus 37.6%; static-obstacle arrival is 50.4% versus 80%. A 48-run equal-interaction study in static and crossing tasks finds no consistent advantage of full gating over always-on correction. Navigation-only control also achieves full crossing arrival, and the learned gate exponent remains close to its initial value. Simulated braking, turning, and paired threshold tests characterize the numerical maneuvering and risk scales. The contribution is an explicitly constrained actor decomposition and an empirical assessment of its limits; the results do not establish an additional performance benefit from the complete adaptive gate in these benchmark families.