  In this study, we present two distinct approaches within the realm of Deep
Reinforcement Learning (Deep-RL) aimed at enhancing mapless navigation for a
ground-based mobile robot. The research methodology primarily involves a
comparative analysis between a Deep-RL strategy grounded in the foundational
Deep Q-Network (DQN) algorithm, and an alternative approach based on the Double
Deep Q-Network (DDQN) algorithm. The agents in these approaches leverage 24
measurements from laser range sampling, coupled with the agent's positional
differentials and orientation relative to the target. This amalgamation of data
influences the agents' determinations regarding navigation, ultimately
dictating the robot's velocities. By embracing this parsimonious sensory
framework as proposed, we successfully showcase the training of an agent for
proficiently executing navigation tasks and adeptly circumventing obstacles.
Notably, this accomplishment is attained without a dependency on intricate
sensory inputs like those inherent to image-centric methodologies. The proposed
methodology is evaluated in three different real environments, revealing that
Double Deep structures significantly enhance the navigation capabilities of
mobile robots compared to simple Q structures.
