  Deep Reinforcement Learning (Deep-RL) techniques for motion control have been
continuously used to deal with decision-making problems for a wide variety of
robots. Previous works showed that Deep-RL can be applied to perform mapless
navigation, including the medium transition of Hybrid Unmanned Aerial
Underwater Vehicles (HUAUVs). These are robots that can operate in both air and
water media, with future potential for rescue tasks in robotics. This paper
presents new approaches based on the state-of-the-art Double Critic
Actor-Critic algorithms to address the navigation and medium transition
problems for a HUAUV. We show that double-critic Deep-RL with Recurrent Neural
Networks using range data and relative localization solely improves the
navigation performance of HUAUVs. Our DoCRL approaches achieved better
navigation and transitioning capability, outperforming previous approaches.
