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We present an approach for agents to learn representations of a global mapfrom sensor data, to aid their exploration in new environments. To achievethis, we embed procedures mimicking that of traditional SimultaneousLocalization and Mapping (SLAM) into the soft attention based addressing ofexternal memory architectures, in which the external memory acts as an internalrepresentation of the environment. This structure encourages the evolution ofSLAM-like behaviors inside a completely differentiable deep neural network. Weshow that this approach can help reinforcement learning agents to successfullyexplore new environments where long-term memory is essential. We validate ourapproach in both challenging grid-world environments and preliminary Gazeboexperiments. A video of our experiments can be found at: https://goo.gl/G2Vu5y.

Neural SLAM: Learning to Explore with External Memory