  Many autonomous systems face safety challenges, requiring robust closed-loop
control to handle physical limitations and safety constraints. Real-world
systems, like autonomous ships, encounter nonlinear dynamics and environmental
disturbances. Reinforcement learning is increasingly used to adapt to complex
scenarios, but standard frameworks ensuring safety and stability are lacking.
Predictive Safety Filters (PSF) offer a promising solution, ensuring constraint
satisfaction in learning-based control without explicit constraint handling.
This modular approach allows using arbitrary control policies, with the safety
filter optimizing proposed actions to meet physical and safety constraints. We
apply this approach to marine navigation, combining RL with PSF on a simulated
Cybership II model. The RL agent is trained on path following and collision
avpodance, while the PSF monitors and modifies control actions for safety.
Results demonstrate the PSF's effectiveness in maintaining safety without
hindering the RL agent's learning rate and performance, evaluated against a
standard RL agent without PSF.
