  Deterministic and Stochastic techniques in Deep Reinforcement Learning
(Deep-RL) have become a promising solution to improve motion control and the
decision-making tasks for a wide variety of robots. Previous works showed that
these Deep-RL algorithms can be applied to perform mapless navigation of mobile
robots in general. However, they tend to use simple sensing strategies since it
has been shown that they perform poorly with a high dimensional state spaces,
such as the ones yielded from image-based sensing. This paper presents a
comparative analysis of two Deep-RL techniques - Deep Deterministic Policy
Gradients (DDPG) and Soft Actor-Critic (SAC) - when performing tasks of mapless
navigation for mobile robots. We aim to contribute by showing how the neural
network architecture influences the learning itself, presenting quantitative
results based on the time and distance of navigation of aerial mobile robots
for each approach. Overall, our analysis of six distinct architectures
highlights that the stochastic approach (SAC) better suits with deeper
architectures, while the opposite happens with the deterministic approach
(DDPG).
