# Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1908.09381 · HTML: https://mlsanity.com/paper/1908.09381/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Forward and Backward Bellman equations improve the efficiency of EM algorithm for DEC-POMDP](https://mlsanity.com/paper/2103.10752/) (2021)
- [M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling](https://mlsanity.com/paper/2403.13728/) (2024)

## Abstract (arXiv)

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.