  Aiming at a comprehensive and concise tutorial survey, recap of variational
inference and reinforcement learning with Probabilistic Graphical Models are
given with detailed derivations. Reviews and comparisons on recent advances in
deep reinforcement learning are made from various aspects. We offer detailed
derivations to a taxonomy of Probabilistic Graphical Model and Variational
Inference methods in deep reinforcement learning, which serves as a
complementary material on top of the original contributions.
