# A Deep Reinforcement Learning Approach for Active SLAM
## 1 Introduction
In this case, they combined DRL with exploration graphs in order to achieve true exploration policies based on the map's uncertainty (T-optimality), outperforming random and nearest frontier agents. They showed good generalization results with higher dimensional state spaces. However, the simulation environment used was a simple grid world in which obstacle avoidance was not needed and only landmark positions were changed. In the literature, experiments are usually conducted in extremely-simplified simulation environments, such as grid-worlds, where the agent moves between cells in a discrete fashion, and sensors are never modelled. In these cases, the inputs to the DNN range from simple occupancy-grid maps [33] to combinations of frontier's and robot's groundtruth locations [32; 34]. More complex and realistic scenarios, although seldom used, are required to feed images [19; 22] or laser measurements [35] to the network. In such cases, it is worth noticing the complexity tackled; not only in the interaction between the simulator and the training algorithm but also in the physics of the robot movement, the environment itself and the sensors modelled (and thus their probabilistic behavior). Knowledge transfer to real environments is yet to be evaluated due to the stage these approaches are at, and only a few of them have effectively addressed it for navigation--and not exploration--purposes [19; 35]. Over the past few years, the basics of DRL have proved to have a great potential in navigation, mapping and even exploration tasks. In this work, we aim to study that potential for the active SLAM decision making problem. In this way, one of the main drawbacks of traditional active SLAM methods is relaxed by transferring the intensive computations to the DNN training phase, requiring only feed-forward propagation during evaluation. The paradigm is formulated as a multi-reward DRL problem, on the basis of classical multi-objective optimization approaches and TOED. On top of Gazebo simulator, a state-of-the-art DQN is embedded within a Robot Operating System (ROS) framework. Thus, the use of both an _uncertainty-based_ reward and a complex simulator are jointly taken into account. Trained agents become capable of making quasi-optimal decisions in order to navigate and explore an environment, just from raw laser measurements. Acquired knowledge transfer is tested in previously unseen scenarios to clarify their generalization ability. Also, traditionally extrinsic and _uncertainty-based_ rewards are compared to evidence the value of the proposed approach. Table 1 summarizes the differences between our approach and other mentioned literature. It can be noticed that true uncertainty metrics are needed in order to tackle Active SLAM. This section provided a brief but comprehensive introduction to the problem tackled. The rest of the paper is organized as follows. In Section 2, active SLAM problem is formulated and the proposed DRL approach is presented. The experimental setup (consisting of the simulation, SLAM and decision making modules) and he results obtained and their discussion are shown in Sections 3 and 4. Finally, the paper conclusions are in Section 5.