入力ゲート付きニューラルネットワークとそのエージェントの行動学習への応用

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  • Neural Networks with Input Gates and Their Applications to Behavior Learning of Agents
  • ニュウリョク ゲート ツキ ニューラル ネットワーク ト ソノ エージェント ノ コウドウ ガクシュウ エ ノ オウヨウ

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Neural networks with input gates are proposed for behavior learning of agents. The networks are equipped with gates on their input channels that pass input signals when they are neccesary. A gate opens and closes depending on the current values of input signals. The dependence is automatically determined based on training data. They are applied to behavior learning of agents in the reinforcement learning framework. The gate openings provide the generalized information about the significance of each input signal, which reduces the size of region to be explored and can be exploited to speed up the subsequent learning in other environments.

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