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 · We extend the capabilities of neural networks by coupling them to external memory resources which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable endtoend allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer

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 · Open source interface to reinforcement learning tasks. The gym library provides an easytouse suite of reinforcement learning tasks.. import gym env = ("CartPolev1") observation = () for _ in range(1000) () action = _() # your agent here (this takes random actions) observation reward done info = (action) if done observation = env

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 · Open source interface to reinforcement learning tasks. The gym library provides an easytouse suite of reinforcement learning tasks.. import gym env = ("CartPolev1") observation = () for _ in range(1000) () action = _() # your agent here (this takes random actions) observation reward done info = (action) if done observation = env

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Neural Turing Machines

 · We extend the capabilities of neural networks by coupling them to external memory resources which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable endtoend allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer

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