  Maritime autonomous transportation has played a crucial role in the
globalization of the world economy. Deep Reinforcement Learning (DRL) has been
applied to automatic path planning to simulate vessel collision avoidance
situations in open seas. End-to-end approaches that learn complex mappings
directly from the input have poor generalization to reach the targets in
different environments. In this work, we present a new strategy called
state-action rotation to improve agent's performance in unseen situations by
rotating the obtained experience (state-action-state) and preserving them in
the replay buffer. We designed our model based on Deep Deterministic Policy
Gradient, local view maker, and planner. Our agent uses two deep Convolutional
Neural Networks to estimate the policy and action-value functions. The proposed
model was exhaustively trained and tested in maritime scenarios with real maps
from cities such as Montreal and Halifax. Experimental results show that the
state-action rotation on top of the CVN consistently improves the rate of
arrival to a destination (RATD) by up 11.96% with respect to the Vessel
Navigator with Planner and Local View (VNPLV), as well as it achieves superior
performance in unseen mappings by up 30.82%. Our proposed approach exhibits
advantages in terms of robustness when tested in a new environment, supporting
the idea that generalization can be achieved by using state-action rotation.
