Policy teaching through reward function learning

@inproceedings{Zhang2009PolicyTT,
  title={Policy teaching through reward function learning},
  author={Haoqi Zhang and David C. Parkes and Yiling Chen},
  booktitle={EC},
  year={2009}
}
Policy teaching considers a Markov Decision Process setting in which an interested party aims to influence an agent's decisions by providing limited incentives. In this paper, we consider the specific objective of inducing a pre-specified desired policy. We examine both the case in which the agent's reward function is known and unknown to the interested party, presenting a linear program for the former case and formulating an active, indirect elicitation method for the latter. We provide… CONTINUE READING
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