This master s thesis consists of an advanced study on deep learning by visual reinforcement for autonomous robots through transfer learning techniques. The simulation environments tested in this study are highly realistic environments where the challenge of the robot was to learn and tranfer knowledge in different contexts to take advantage of the experiencia of previous environments in future environments. This type of approach besides adding knowledge to the autonomous robot reduces the number of training epochs the algorithm, even in complex environments, justifying the use of transfer learning techniques.