Reinforcement Learning Agent for Path Planning with Expert Demonstration

The problem of path planning is a challenging task for mobile robots. A practical example can be seen in the robots commonly employed in warehouses: they must navigate to pick up goods and move them to certain locations. Therefore, the robot needs a method of moving from an initial location in the warehouse to a final location repeatedly. In this paper, we propose a technique that allows a robot to path plan in generalized environments, from different starting and goal locations. The method is based on a graph representation of the environment, and is capable of finding the shortest path between two points in the environment. In order to generalize appropriately, we show that a neural network is able to effectively choose the correct actions to take at each time step in the path planning problem.