Functional Representation For Nonlinear Reinforcement Learning

Modified: 2016/03/02 15:58 by admin - Uncategorized
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.


  • Boosting nonparametric policies: In the AAMAS'16 (PDF) paper, we propose PolicyBoost to learn functional policies with less overfitting.

  • Improving speed by napping: 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 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.


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