Practical Reinforcement Learning in Continuous Spaces

@inproceedings{Smart2000PracticalRL,
  title={Practical Reinforcement Learning in Continuous Spaces},
  author={William D. Smart and Leslie Pack Kaelbling},
  booktitle={ICML},
  year={2000}
}
Dynamic control tasks are good candidates for the application of reinforcement learning techniques. However, many of these tasks inherently have continuous state or action variables. This can cause problems for traditional reinforcement learning algorithms which assume discrete states and actions. In this paper, we introduce an algorithm that safely approximates the value function for continuous state control tasks, and that learns quickly from a small amount of data. We give experimental… CONTINUE READING
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