Efficient Reinforcement Learning

@inproceedings{Fiechter1994EfficientRL,
  title={Efficient Reinforcement Learning},
  author={Claude-Nicolas Fiechter},
  booktitle={COLT},
  year={1994}
}
In this paper we propose a new formal model for studying reinforcement learning, based on Valiant's PAC framework. In our model the learner does not have direct access to every state of the environment. Instead, every sequence of experiments starts in a fixed initial state and the learner is provided with a “reset” operation that interrupts the current sequence of experiments and starts a new one (from the initial state). We do not require the agent to learn the optimal policy but only a good… CONTINUE READING

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