Principled Methods for Advising Reinforcement Learning Agents

@inproceedings{Wiewiora2003PrincipledMF,
  title={Principled Methods for Advising Reinforcement Learning Agents},
  author={Eric Wiewiora and Garrison W. Cottrell and Charles Elkan},
  booktitle={ICML},
  year={2003}
}
An important issue in reinforcement learning is how to incorporate expert knowledge in a principled manner, especially as we scale up to real-world tasks. In this paper, we present a method for incorporating arbitrary advice into the reward structure of a reinforcement learning agent without altering the optimal policy. This method extends the potentialbased shaping method proposed by Ng et al. (1999) to the case of shaping functions based on both states and actions. This allows for much more… CONTINUE READING
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