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A Deep Reinforcement Learning Approach for Active SLAM
2020
Applied Sciences
In this paper, we formulate the active SLAM paradigm in terms of model-free Deep Reinforcement Learning, embedding the traditional utility functions based on the Theory of Optimal Experimental Design in rewards, and therefore relaxing the intensive computations of classical approaches. We validate such formulation in a complex simulation environment, using a state-of-the-art deep Q-learning architecture with laser measurements as network inputs. Trained agents become capable not only to learn a
doi:10.3390/app10238386
fatcat:sk5plhifsbckvckown3u4gneyu