Reinforcement Learning-SLAM for finding minimum cost path and mapping

In this work, we propose the integration of two of the most widely used approaches for the implementation of autonomous navigation systems: the reinforcement learning for path finding, along with SLAM (Simultaneous Localization and Mapping) type algorithms for localization and mapping of the environment. These two approaches are integrated to address the problem of how a robot should explore an unknown and dynamic environment while it collects perception features in order to locate itself and, at the same time, to obtain information clues about cost traversability of an area. So, when a robot is exploring and mapping with a SLAM algorithm it is also learning to associate perception features with costs and actions to find optimal paths from the starting point to the goal point in dynamical environments.