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Human-level control through deep reinforcement learning
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 using the same algorithm, network architecture and hyperparameters. Expand
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Overcoming catastrophic forgetting in neural networks
We present an algorithm, EWC, that allows knowledge of previous tasks to be protected during new learning, thereby avoiding catastrophic forgetting. Expand
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Mastering the game of Go with deep neural networks and tree search
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Expand
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Mastering the game of Go without human knowledge
A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Expand
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Hybrid computing using a neural network with dynamic external memory
We introduce a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Expand
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Patients with hippocampal amnesia cannot imagine new experiences
Amnesic patients have a well established deficit in remembering their past experiences. Surprisingly, however, the question as to whether such patients can imagine new experiences has not beenExpand
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Parallel WaveNet: Fast High-Fidelity Speech Synthesis
This paper introduces Probability Density Distillation, a new method for training a parallel feed-forward network from a trained WaveNet with no significant difference in quality. Expand
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Using Imagination to Understand the Neural Basis of Episodic Memory
Functional MRI (fMRI) studies investigating the neural basis of episodic memory recall, and the related task of thinking about plausible personal future events, have revealed a consistent network ofExpand
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Deconstructing episodic memory with construction
It has recently been observed that the brain network supporting recall of episodic memories shares much in common with other cognitive functions such as episodic future thinking, navigation andExpand
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Noisy Networks for Exploration
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration. Expand
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