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  • Human-level control through deep reinforcement learning | Nature
    Now a team working at Google's DeepMind subsidiary has developed an artificial agent — dubbed a deep Q-network — that learns to play 49 classic Atari 2600 'arcade' games directly from sensory
  • Human-level control through deep reinforcement learning . . .
    Here we use recent advances in training deep neural networks9–11 to develop a novel artificial agent, termed a deep Q-network, that can learnsuccessfulpoliciesdirectlyfromhigh-dimensionalsensoryinputs using end-to-end reinforcement learning
  • Human-level control through deep reinforcement learning
    Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning
  • Human-level control through deep reinforcement learning
    In this thesis, Deep Deterministic Policy Gradients, a deep reinforcement learning method for continuous control, has been implemented, evaluated and put into context to serve as a basis for further research in the field
  • Human-level control through deep reinforcement learning.
    Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning
  • Human-level control through deep reinforcement learning - ADS
    Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning
  • Human-level control through deep reinforcement learning
    An artificial agent is developed that learns to play a diverse range of classic Atari 2600 computer games directly from sensory experience, achieving a performance comparable to that of an expert human player; this work paves the way to building general-purpose learning algorithms that bridge the divide between perception and action
  • [1312. 5602] Playing Atari with Deep Reinforcement Learning
    We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them
  • Human-level control through deep reinforcement learning - Nature
    We demon-strate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level
  • Human-level control through deep reinforcement learning
    百度学术集成海量学术资源，融合人工智能、深度学习、大数据分析等技术，为科研工作者提供全面快捷的学术服务。 在这里我们保持学习的态度，不忘初心，砥砺前行。





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