Reinforcement learning: Computational theory and biological mechanisms.

  title={Reinforcement learning: Computational theory and biological mechanisms.},
  author={Kenji Doya},
  journal={HFSP journal},
  volume={1 1},
Reinforcement learning is a computational framework for an active agent to learn behaviors on the basis of a scalar reward signal. The agent can be an animal, a human, or an artificial system such as a robot or a computer program. The reward can be food, water, money, or whatever measure of the performance of the agent. The theory of reinforcement learning, which was developed in an artificial intelligence community with intuitions from animal learning theory, is now giving a coherent account… CONTINUE READING

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