Description

Recent advancements in external memory based neural networks have shown promise

in solving tasks that require precise storage and retrieval of past information. Re-

searchers have applied these models to a wide

Recent advancements in external memory based neural networks have shown promise

in solving tasks that require precise storage and retrieval of past information. Re-

searchers have applied these models to a wide range of tasks that have algorithmic

properties but have not applied these models to real-world robotic tasks. In this

thesis, we present memory-augmented neural networks that synthesize robot navigation policies which a) encode long-term temporal dependencies b) make decisions in

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Date Created
  • 2018
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  • Text
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    • Masters Thesis Computer Science 2018

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