Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning
This is an incremental work that serves as a tutorial and survey for researchers and practitioners in machine learning, summarizing existing methods rather than introducing novel contributions.
The paper provides a tutorial and survey on probabilistic graphical models and variational inference in deep reinforcement learning, offering detailed derivations and comparisons of recent advances without presenting new experimental results.
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.