Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning

@inproceedings{Cheng2011PreferenceBasedPI,
  title={Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning},
  author={Weiwei Cheng and Johannes F{\"u}rnkranz and Eyke H{\"u}llermeier and Sang-Hyeun Park},
  booktitle={ECML/PKDD},
  year={2011}
}
This paper makes a first step toward the integration of two subfields of machine learning, namely preference learning and reinforcement learning (RL). An important motivation for a "preference-based" approach to reinforcement learning is a possible extension of the type of feedback an agent may learn from. In particular, while conventional RL methods are essentially confined to deal with numerical rewards, there are many applications in which this type of information is not naturally available… CONTINUE READING
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