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- W2585193509 abstract "We develop a general problem setting for training and testing the ability of agents to gather information efficiently. Specifically, we present a collection of tasks in which success requires searching through a partially-observed environment, for fragments of information which can be pieced together to accomplish various goals. We combine deep architectures with techniques from reinforcement learning to develop agents that solve our tasks. We shape the behavior of these agents by combining extrinsic and intrinsic rewards. We empirically demonstrate that these agents learn to search actively and intelligently for new information to reduce their uncertainty, and to exploit information they have already acquired." @default.
- W2585193509 created "2017-02-10" @default.
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- W2585193509 date "2016-11-05" @default.
- W2585193509 modified "2023-09-27" @default.
- W2585193509 title "Towards Information-Seeking Agents" @default.
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