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- W2618235849 abstract "A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world. In this work, we address the problem of visual semantic planning: the task of predicting a sequence of actions from visual observations that transform a dynamic environment from an initial state to a goal state. Doing so entails knowledge about objects and their affordances, as well as actions and their preconditions and effects. We propose learning these through interacting with a visual and dynamic environment. Our proposed solution involves bootstrapping reinforcement learning with imitation learning. To ensure cross task generalization, we develop a deep predictive model based on successor representations. Our experimental results show near optimal results across a wide range of tasks in the challenging THOR environment." @default.
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- W2618235849 date "2017-05-23" @default.
- W2618235849 modified "2023-09-25" @default.
- W2618235849 title "Visual Semantic Planning using Deep Successor Representations" @default.
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- W2618235849 doi "https://doi.org/10.48550/arxiv.1705.08080" @default.
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