DQN Paper


Human-level control through deep reinforcement learning

This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Deep Q-network (DQN)

  • Combine reinforcement learning with deep neural networks.
  • Deep convolutional network
    • which uses hierarchical layers of tiled convolutional filters to mimic the effects of receptive fields—inspired by Hubel and Wiesel’s seminal work on feedforward processing in early visual cortex
    • thereby exploiting the local spatial correlations present in images
    • and building in robustness to natural transformations such as changes of viewpoint or scale.

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