Hilbert Space Embeddings of POMDPs

Understand

A nonparametric approach for policy learning for POMDPs is proposed.

• The approach represents distributions over the states, observations, and actions as embeddings in feature spaces, which are reproducing kernel Hilbert spaces.
• Distributions over states given the observations are obtained by applying the kernel Bayes' rule to these distribution embeddings.
• Policies and value functions are defined on the feature space over states, which leads to a feature space expression for the Bellman equation.