Simple statistical gradient-following algorithms for connectionist reinforcement learning

@article{Williams1992SimpleSG,
  title={Simple statistical gradient-following algorithms for connectionist reinforcement learning},
  author={Ronald J. Williams},
  journal={Machine Learning},
  year={1992},
  volume={8},
  pages={229-256}
}
This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units. These algorithms, called REINFORCE algorithms, are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reinforcement tasks, and they do this without explicitly computing gradient estimates or even storing information from which… CONTINUE READING

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