Human Level Control Through Deep Reinforcement Learning-PDF Free Download

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2.3 Deep Reinforcement Learning: Deep Q-Network 7 that the output computed is consistent with the training labels in the training set for a given image. [1] 2.3 Deep Reinforcement Learning: Deep Q-Network Deep Reinforcement Learning are implementations of Reinforcement Learning methods that use Deep Neural Networks to calculate the optimal policy.

1 Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529{533 (2015) 2 Lin, L.-J. Reinforcement learning for robots using neural networks. Technical Report, DTIC Document (1993) Dayeol Choi Deep RL Nov. 4th 2016 13 / 13

Mnih et al. Human-level control through deep reinforcement learning, Nature 2015. Experience replay At each time step: . Mnih et al. Human-level control through deep reinforcement learning, Nature 2015. Experience replay At each time step: –Take action a t according to epsilon-g

Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533. Training tricks Issues: a. Data is sequential Experience replay . Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 5

Why Deep? Deep learning is a family of techniques for building and training largeneural networks Why deep and not wide? –Deep sounds better than wide J –While wide is always possible, deep may require fewer nodes to achieve the same result –May be easier to structure with human

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In 2015, the Google Deepmind group published the paper Human-level control through deep rein-forcement learning [25] in Nature, which leads to a breakthrough in this area. They demonstrate a deep Q-network that could overcome these challenges and learn control

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Deep neural networks has enabled signicant progress in re-inforcement learning research in recent years. The semi-nal work Deep Q-Networks (DQN)[Mnihet al., 2015] suc-cessfully learns to play Atari games at or exceeding human-level performance by combining deep convolution neural network[LeCunet al., 1995] and Q-learning[Watkins and Dayan, 1992].

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Deep learning refers to a set of machine learning techniques that learn multiple levels of representations in deep archi-tectures. In this section, we will present a brief overview of two well-established deep architectures: deep belief net

Deep Convolutional Neural Networks have been shown to be very useful for visual recognition tasks. AlexNet [17] won the ImageNet Large Scale Visual Recognition Chal-lenge [22] in 2012, spurring a lot of interest in using deep learning to solve challenging problems. Since then, deep learning

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Deep Reinforcement Learning: Reinforcement learn-ing aims to learn the policy of sequential actions for decision-making problems [43, 21, 28]. Due to the recen-t success in deep learning [24], deep reinforcement learn-ing has aroused more and more attention by combining re-inforcement learning with deep neural networks [32, 38].

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2.2 Deep Learning Recently, deep learning methods have been successfully applied to a variety of language and information retrieval applications [1][4][7][19][22][23][25]. By exploiting deep architectures, deep learning techniques are able to discover from training data the

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VolodymyrMnih, KorayKavukcuoglu, David Silver et al. Human-level control through deep reinforcement learning. Nature 2015. DQN (NIPS 2013) is the beginning of the entire deep reinforcement learning sub-area. VolodymyrMnih, KorayKavukcuoglu, David Silver et al. Playing Atari with

learning: early image-based Q-learning method using autoencoders to construct embeddings Mnih et al. (2013). Human-level control through deep reinforcement learning: Q-learning with convolutional networks for playing Atari. Van Hasselt, Guez, Silver. (2015). Deep reinforcement lea