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.
@article{arxiv.1908.09381,
title = {Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning},
author = {Xudong Sun and Bernd Bischl},
journal= {arXiv preprint arXiv:1908.09381},
year = {2019}
}
Comments
2019 IEEE Symposium on Computational Intelligence, Symposium on Adaptive Dynamic Programming and Reinforcement Learning