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Functional Representation For Nonlinear Reinforcement Learning
Reinforcement learning seeks for a policy that receives the highest total reward from its environement. Functional representation is a powerful tool to approximate complex functions, which can help learn complex policies for fitting practical environments. '''Papers''': * <b>Boosting nonparametric policies</b>: In the [{UP}papers/aamas16_policyboost.pdf|AAMAS'16 (PDF)] paper, we propose PolicyBoost to learn functional policies with less overfitting. * <b>Improving speed by napping</b>: By functional representation, a policy can be a sum of many basis functions, which causes a high time cost especially for reinforcement learning. In the [{UP}papers/aamas14-nap.pdf|AAMAS'14 (PDF)] paper, we tackle this time cost barrier by the ''napping'' mechanism, which results a significant improvement in time as well as a potential improvement in total reward. '''Codes''': * [code_funcpolicy|Nonlinear reinforcement learning with functional representation]
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