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Published on Sep 8, 2017
We address the problem of visual semantic planning: the task of predicting a sequence of actions that change the state of the visual world from an initial state to a goal state. We propose learning through interaction with a visual and dynamic environment. Our experimental results show near optimal results across a wide range of tasks in the challenging THOR environment.