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- W4300908595 abstract "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." @default.
- W4300908595 created "2022-10-04" @default.
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- W4300908595 date "2022-06-01" @default.
- W4300908595 modified "2023-10-18" @default.
- W4300908595 title "Reinforcement Learning Agent for Path Planning with Expert Demonstration" @default.
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- W4300908595 doi "https://doi.org/10.1109/compsac54236.2022.00158" @default.
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