In this letter, we propose a robust approach to train map-less navigation policies that rely on variable size 2D point clouds, using Deep Reinforcement Learning (Deep RL). The navigation policies are trained in simulations ...

This paper presents a study of deep reinforcement learning techniques that uses parallel distributional actor-critic networks to navigate terrestrial mobile robots. The proposed approaches are developed taking into account ...

The work presented in this text consists of a study on deep reinforcement learning applied to mapless navigation of drones in three dimensions. For this, two methods were used, D4PG and DSAC, both with parallelization ...

This work presents two Deep Reinforcement Learning (Deep-RL) approaches to enhance the problem of mapless navigation for a terrestrial mobile robot. The methodology focus on comparing a Deep-RL technique based on the Deep ...

The problem of autonomous robot navigation in internal environments must overcome various difficulties such as the dimensionality of the data, the computational cost and the possible presence of mobile objects. This thesis ...

This degree project presents the design and implementation of a position controller for a multi-rotor UAV based on deep neural networks and trained by for a multi-rotor UAV based on deep neural networks and trained by ...