Reinforcement Learning in Continuous State and Action Spaces

@inproceedings{Hasselt2012ReinforcementLI,
  title={Reinforcement Learning in Continuous State and Action Spaces},
  author={Hado Philip van Hasselt},
  booktitle={Reinforcement Learning},
  year={2012}
}
Many traditional reinforcement-learning algorithms have been designed for problems with small finite state and action spaces. Learning in such discrete problems can been difficult, due to noise and delayed reinforcements. However, many real-world problems have continuous state or action spaces, which can make learning a good decision policy even more involved. In this chapter we discuss how to automatically find good decision policies in continuous domains. Because analytically computing a good… CONTINUE READING

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