Importance sampling for reinforcement learning with multiple objectives

@inproceedings{Shelton2001ImportanceSF,
  title={Importance sampling for reinforcement learning with multiple objectives},
  author={Christian R. Shelton},
  year={2001}
}
This thesis considers three complications that arise from applying reinforcement learning to a real-world application. In the process of using reinforcement learning to build an adaptive electronic market-maker, we find the sparsity of data, the partial observability of the domain, and the multiple objectives of the agent to cause serious problems for existing reinforcement learning algorithms. We employ importance sampling (likelihood ratios) to achieve good performance in partially observable… CONTINUE READING
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