Autonomous Sailboat Control based on Reinforcement Learning for Navigation in Variable Conditions

In recent times, unmanned autonomous sailboats have gained significant relevance due to the energy advantages they offer compared to other vessels, as their main power source is the wind, allowing them to sail for indefinite periods. One of the main challenges these sailboats face is navigating in diverse weather conditions, which has led to the need for developing an adaptive controller capable of handling these situations. This work proposes a solution to this problem by generating an adaptive controller using reinforcement learning combined with imitation learning and utilizing a simulator for training and evaluation. The conducted experiments demonstrate that the generated controllers can navigate in multiple environmental conditions and perform better than the static controller used as a reference. It is worth noting that only reinforcement learning is employed in training the resulting controllers, as imitation learning did not prove beneficial. The code can be found in the repository [1].