Policy Shaping: Integrating Human Feedback with Reinforcement Learning

@inproceedings{Griffith2013PolicySI,
  title={Policy Shaping: Integrating Human Feedback with Reinforcement Learning},
  author={Shane Griffith and Kaushik Subramanian and Jonathan Scholz and Charles Lee Isbell and Andrea Lockerd Thomaz},
  booktitle={NIPS},
  year={2013}
}
A long term goal of Interactive Reinforcement Learning is to incorporate nonexpert human feedback to solve complex tasks. Some state-of -the-art methods have approached this problem by mapping human information t o rewards and values and iterating over them to compute better control polici es. In this paper we argue for an alternate, more effective characterization of human feedback: Policy Shaping. We introduce Advise, a Bayesian approach that attempts to maximize the information gained from… CONTINUE READING
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