## 2 Literature Review  
There are different approaches to establishing coordinated movements between the base and the arm depending on the application. [PERSON] et al. [4] specified three types of cooperation, which differ in the degree of cooperation, the associated complexity and the potential for manipulation capabilities: (1) Loose cooperation is when the mobile base and the manipulator are considered two separate systems, and the base serves as transport. There are several very well-known methods for navigation and trajectory planning [5]. (2) Full cooperation is when the two systems are seen as one (with nine degrees of freedom), and both the base and the arm move simultaneously to position the tool center. There are various approaches to solve the path planning problem [6; 7], however, in these cases, the computational cost is very high. (3) Transparent cooperation is the combination of the previous two: the manipulator control compensates for the base and the base moves accordingly to maximise a cost function related to the positioning of the manipulator to perform the task. In [4], an approach to this type of control is proposed. In general, current robotic solutions follow the loose cooperation approach. The coordination of the two subsystems for mobile manipulation depends on the task that needs to be solved. [PERSON] et al. [8] addressed the issue of pick and place where the robotic system must take a can of a known position and place it in another combining the movement of the arm and the base. In [9], the problem of opening doors is considered where the movement of the arm and base are coupled by sensing the forces generated between the arm and the door, and coordinating the forward movement of the base. One of the principal goals of artificial intelligence (AI) is to enable agents to learn from their experience to become fully autonomous. This experience is obtained by interacting with the environment. Thus, the agent should continue improving its behaviour through trial and error until it behaves optimally. Deep learning enables reinforcement learning to face decision-making problems that were previously infeasible. The union of both of them, namely DRL, makes it possible to learn complex policies guided by a reward signal and has been applied to learn multiple complex decision making tasks in a wide variety of areas. For instance, in [10], the authors learn a chess player agent by self playing that is able to defeat a world champion. In [11], DRL is used to learn an intelligent dialogue generator agent. It also has been successfully applied to computer vision, for example to train a 2D object tracker agent that outperforms state-of-the-art real-time trackers [12]. The field of DRL applied to robotics is becoming more and more popular, and the number of published papers related to that topic is increasing quickly. Applications extend from manipulation [13; 14; 15; 16] to autonomous navigation [17; 18] and locomotion [19; 20]. DRL has been successfully applied in some robotic path planning and control problems. For example, [PERSON] et al. [16] used DRL to learn a low-level controller that is able to control a robotic arm to perform door opening. On the one hand, 25 variables are used to represent the environment's state. More specifically, those variables correspond to the joint angles and their time derivatives, the goal position and the door angle together with the handle angle. On the other hand, the action is represented by the torque for each joint. Robot navigation is another area where DRL also has been applied successfully; for example, in [18], DRL is applied to learn a controller to perform mapless navigation with a mobile robot. In this work, a low dimensionality state representation is also used, specifically 10-dimensional laser range findings, the previous action and the relative target position. The action representation includes linear and angular velocities of the differential robot. Alternatively, several methods use 2D or 3D images instead of low-dimensional state representations.