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
:
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.
Codes
:
Nonlinear reinforcement learning with functional representation