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- W4225545903 abstract "AbstractThis chapter aims to explain the fifth intelligence layer of the machine brain—automatized execution, which is realized through information acquisition, management, utilization and then be utilized to interpret machine intelligence associated with the current technologies (the system-level intelligence), emerging technologies (the behavior-level intelligence) and beyond (the thinking-level intelligence). The meta-learning thoughts introduced in Chap. 4 are utilized. Deep RL uses neural networks to approximate the learning data, which develops rapidly and makes the sequential decision making achieve preliminary results. However, deep reinforcement learning is overly dependent on a large amount of training and requires precise rewards. For many problems in the real world, such as robot learning, there is generally no good reward, no infinite training, which requires the ability to learn quickly, so there is meta-learning. This paper proposes a deep reinforcement learning model based on meta-learning, called Meta Q-Network (MQN). The model uses LSTM-based meta-learner to update the Q network. We also present a brief explanation for how to extend the fifth intelligence layer with extenics." @default.
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- W4225545903 date "2022-01-01" @default.
- W4225545903 modified "2023-09-28" @default.
- W4225545903 title "The Fifth Intelligence Layer—Automatized Execution" @default.
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- W4225545903 doi "https://doi.org/10.1007/978-981-19-0272-7_6" @default.
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