Importance Sampling for Reinforcement Learning with Multiple Objectives
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...
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| Language: | en_US |
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2004
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| Online Access: | http://hdl.handle.net/1721.1/5568 |